Comparison between molecular chemistry and materials chemistry context for different concepts.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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The term Green Chemistry refers to the strategies for the production and use of safer chemical products as replacements for hazardous substances. In this sense, hazardous substances can be defined in a broad way as any substance representing any physical, such as injury as a result of short or long term exposure; environmental, such as water or air pollution; or toxicological risks, such as mutations or cancer [1].
Although the search for safer reagents and solvents has been an ongoing process in modern chemistry, the term Green Chemistry was coined at the beginning of the 1990’s decade. Soon after the establishment of the Pollution Prevention Act of 1990 [2]. Among different proposals, this Act included source reduction as desirable in opposition to waste management in addition to pollution control and more cost-effective production and operation procedures to reduce or prevent pollution generated by industries.
In 1998, Anastas and Warner published the book Green Chemistry: Theory and Practice. This book presented for the first time the
The 12 Principles of Green Chemistry function like an instruction manual for those professionals willing to develop products and processes more aligned with the Green Chemistry concept. These Principles also have contributed to the popularization of Green Chemistry, since they work as a concise and accessible consulting resource. As the 12 Principles of Green Chemistry will be continuously recalled throughout this chapter, they are presented here to provide a quick reference to the readers [3].
Principle 1—Prevent the Waste.
Principle 2—Atom Economy.
Principle 3—Less Hazardous Chemical Synthesis.
Principle 4—Designing Safer Chemicals.
Principle 5—Safer Solvents and Auxiliaries.
Principle 6—Design for Energy Efficiency.
Principle 7—Use of Renewable Feedstocks.
Principle 8—Reduce Derivatives.
Principle 9—Catalyst reagents are preferred over stoichiometric ones.
Principle 10—Design for Degradation.
Principle 11—Real-time Analysis for Pollution Prevention.
Principle 12—Inherently Safer Chemicals for Accident Prevention.
Since the proposal of the Pollution Prevention Act, the Green Chemistry field has grown substantially in the scientific literature.
Many of the terms and parameters related to Green Chemistry were defined considering a molecular structure as a model to assess the safety and toxicological properties. While dealing with nanomaterials, besides molecular structure, other factors such as being crystalline or amorphous, crystal structure, surface area, particle size, porosity, and so on can play substantial roles regarding how a nanomaterial should be evaluated in relation to its production, life cycle, toxicity, and disposal. Hutchison
Concept | Molecular Context | Materials Context |
---|---|---|
Composition | Defined by molecular formula | Core and surface composition difficult to define; may vary according to sample shape and size |
Size/shape | Defined molecular structure and shape | Often a mixture of sizes and shapes, dependent on synthetic method |
Dispersity | Single and continuous composition and structure | Characterized by distributions of composition and structural features |
Purity | Purification procedure is intimately related to molecular structure (i.e. chromatography) | Small molecule impurities coming from surface coating or unreacted precursors significantly influence properties |
Toxicity | Possible to assess based on molecular structure | It may be inherent to the composition but also can be related to particle size, shape, or surface coating |
Comparison between molecular chemistry and materials chemistry context for different concepts.
Based on this context, this chapter focus on the strategies, perspectives, and advancements of the greener preparation of transition metal chalcogenides (TMCs). TMCs are part of a broad class of materials comprising binary, ternary, quaternary, and multinary sulfides, selenides, and tellurides. These materials have application in different areas such as solar cells, photocatalysis, sensors, photoinduced therapy, and fluorescent labeling [5, 6, 7]. Due to the technological importance of this class of material, it is necessary to find synthetic methods and sophisticated tools to help produce the TMCs nanomaterials through procedures aligned with Green Chemistry.
In this sense, we review the recent literature for the recent advances not only in the chemical synthesis of the TMCs, but also in emerging planning and analysis techniques, such as the design of experiments, life cycle assessment, and machine learning. These emerging techniques can contribute to the further advancement of Green Chemistry.
Any change in the synthetic process that eliminates or replaces a hazardous reagent or solvent [8], or is consistent with one of the 12 Principles of Green Chemistry will likely result in an overall process that is more environmentally friendly and less hazardous. Also, it is important to point out that the changes necessary to make the process greener must not compromise the quality of the final product. Green Chemistry, when successfully implemented, results in the green production of high-performance products. If performance is compromised, then the process does not yield a functional product.
There are many opportunities to make the synthesis of transition metal chalcogenides greener. In this section we outline strategies to
The selection of safer reagents and solvents that are unsuitable for producing high-quality materials represents a waste of time and resources. Thus, the best course of action is to first examine results from related work in order to reasonably predict whether a reaction or procedure will be successful using the greener precursors and solvents. Also, working at small scales in the initial stages can represent an economy of time and resources. If the procedure did not work well on a small scale, then one would not proceed to a larger scale procedure. Finally, careful examination of the safety materials associated with each chemical is crucial for preventing problems arising from the combination of incompatible materials or the production of toxic byproducts. These strategies specifically address Green Chemistry Principles 1, 5, and 12.
The wet-chemical synthesis of transition metal chalcogenides requires some source of energy input (to promote the diffusion process), which is often provided by heating the solution containing the starting materials to temperatures above 200°C. Often, this heating procedure is carried out using a reflux apparatus, which requires the consumption of many liters of water to cool the reflux column. Alternatives to refluxing include reactions assisted by microwaves or ultrasound [10]. Furthermore, procedures that enable the synthesis at lower temperature or even at room temperature represent a greener process. The use of more efficient energy input sources addresses Green Chemistry Principles number 1 and 6.
The reduction or elimination of byproducts can mean little to no post-synthesis purification is required. Indeed, the separation of the desired product from the reaction medium as well as from the undesired byproducts often represents the most waste-generating step. The reduction or elimination of byproducts addresses Green Chemistry Principles 1, 2, 6, and 8.
In the synthesis of transition metal chalcogenides, it is common to use capping agents, which are often surfactants, to obtain a certain size and anisotropic shape for the nanoparticles. In many cases, surfactants are necessary to obtain a particular anisotropic shape. However, in some cases, the growth can be controlled by the solvent, by varying the amount of a certain starting material, or adjusting other parameters like temperature, pH, or ionic strength. Avoiding unnecessary additives means less post-synthesis purification is required, and fewer reagents are required overall.
Nanoparticle synthesis methods commonly produce nanoparticles in some non-polar solvent. To use these nanoparticles for some applications often requires dispersion in a polar solvent. When this happens, it is necessary to replace the capping agent that makes the particle dispersible in the non-polar solvent with another capping agent that makes particle dispersible in a polar solvent. This ligand exchange procedure consumes time and additional solvent and reagents. In many cases, ligand exchange can be avoided by simply choosing a synthetic route that yields the nanoparticles with surface chemistry that is suitable for the final application. Avoiding unneeded additives and unnecessary ligand exchange steps directly address Principles number 2, 5, and 6.
Commonly employed purification procedures include washing nanoparticles with a solvent that can solubilize only the byproducts. Other purification processes are based on the difference in size of the products and byproducts, for instance, the size-exclusion chromatography and dialysis. All these procedures require the use of additional reagents, particularly solvents, which makes the purification procedure one of the most difficult steps to
An ideal synthetic procedure will produce the desired product in both high yield and high purity. Indeed, even trace impurities can drastically compromise the performance of devices. In order to reduce the total solvent required, in dialysis, for example, sequential dialysis against smaller volumes of pure solvent will generate less waste and a product with higher purity.
Judicious selection of solvent may also mean that the post-dialysis solvent could be recycled by passing through a purification column, for example. Alternative purification procedures should be investigated to select the method that will be the greenest possible without compromising the purity of the final product. The use of greener purification procedures addresses Green Chemistry Principles 3, 4, 7, and 12.
Design of Experiment (DoE) approach helps minimize the number of experiments. The experiment minimization agrees with the Green Chemistry Principles 1, 2, 6, 8, and 11. One way to efficiently decrease the number of experiments needed to fully analyze the data is to apply the concept of Design of Experiments (DoE). The DoE consists of a set of statistical techniques where the experiments are planned and performed according to a multivariate approach. The multivariate approach can be understood as an experimental plan where all the possible factors are varied simultaneously [11].
The multivariate approach contrasts with the univariate approach, which is generally known by the acronym OFAT, meaning one factor at a time. The OFAT approach is usually the standard approach in the chemical literature [12]. For instance, suppose that a research group is interested in studying the effect of temperature, pH, and concentration. And the goal of the research is to maximize the yield of the reaction.
According to the OFAT approach, the group would choose, for instance, five temperature levels, 4 pH values levels, and four concentration levels. And then, they would set a temperature and pH, and find an optimal concentration. Next, they will fix this optimal concentration and vary the temperature and pH in all levels, and find an optimal pH value. Then, finally, they will select the optimal concentration and pH, and vary the temperature in all five levels until finding the optimal temperature.
The OFAT approach, although widely used in the literature, has some drawbacks. The first one is the usual large number of experiments to be performed. The second one is that by fixing one level for all the variables, except the one that will be varied in all levels, can lead to a situation where not all possible experimental conditions were explored. Consequently, there is the chance that the most optimal condition determined is not the actual optimal condition. Another consequence of not varying all the variables at the same time is that all factors are not studied in a connected way [13]. Consequently, it is impossible to analyze the effect of the interaction among two or more factors. Ultimately, it hinders the obtaining of a mathematical model that would allow estimating the yield for conditions initially untested.
In contrast, in the multivariate approach for the same situation, only two levels would be selected for each factor (temperature, concentration, or pH). Then, all three factors are varied simultaneously, leading to a total of eight independent experiments. The results obtained for these eight experiments are analyzed according to a set of algorithms. The output of these calculations would allow estimating not only the effect of each factor independently. But also, the effect of all possible combinations of factors two by two, and the combination of the three factors. Then, after determining which factors and interactions have statistically significant effects, it is possible to refine the calculations and obtain an empirical model that would allow estimating the results for an experimental condition initially untested [14, 15].
This DoE explained in this example is called 2
The use of life cycle assessmentFollowing a system thinking approach Life Cycle Assessments (LCA) are designed to evaluate and assess the potential environmental, economic, and societal impacts related to the sourcing of reagents, processing, distribution, use, and disposal of materials [17, 18, 19]. By listing, mapping, and evaluating the safety and suitability of the material and energy inputs, products, and byproducts, it is possible to compare distinctive synthetic methods or different possible products by determining relevant metrics focusing on process intensity (similar to atom economy), toxicity, and cost [20, 21, 22, 23].
LCAs focused on metal nanoparticles have linked high energy consumption to upstream metal refining and been used to screen reducing agents indicating how these methods serve to analyze the literature and help tailor synthetic protocols [24, 25]. LCAs of manufactured photovoltaic cells with chalcogenides have been used to determine whether other components in these systems such as steel or glass contribute to downstream impacts helping to place research in a wider context [18].
While LCA methods are specific and tailored to a given system, examining analyses of metal chalcogenides and related green nanoparticle systems through metrics-based assessments can inform the design of transition metal chalcogenide nanoparticles and help mitigate unwanted impacts [22, 25, 26]. Researchers may look to blend their own experimental data with literature data to support claims of innovation or sustainability with quantitative analyses using a life cycle approach towards making and using nanoparticulate matter [27, 28, 29]. Life Cycle Assessment touches on many of the principles of green chemistry and specifically principles 1, 4, 6, and 10.
Data-driven approaches are found helpful in numerous fields of material science, especially when they are paired with computational methods [30, 31], where the data can be generated in a high-throughput fashion, with consistent quality. Data-driven methods are also beneficial to a traditional synthesis-oriented areas, especially due to digitalization of information (for example, lab notebook) processing the wealth of experimental notes becomes possible [32, 33]. The machine-learning applications in chemistry currently focus on property prediction (ranging from mechanical properties to electronic structure) [34, 35, 36] rather than structure prediction and exploratory synthesis guidance [37, 38, 39, 40, 41]. The areas of machine-learning application in materials science, include solar cells, perovskites, and non-centrosymmetric structures, which echoes with chalcogenides’ typical industrial applications.
Being one of the most rapidly emerging fields nowadays, machine learning quickly went through typical stages of method development and crystallized in the list of best practices for applying machine learning in materials domain [42]. For example, sharing entire code of the model, along with the input data and pre-processing methods, gave research publications transparency and promoted sharing the ideas to the next level. Democratization of data, approaches, and informatics allows domain experts to be part of the machine learning community, even with limited knowledge in computer science.
The main benefits of machine learning methods for materials are: (i) analysis of complex correlations between parameters and output,
Machine-learning approaches can guide us towards the direction of narrowing materials candidate pool, which eventually results in less waste (Principle 1). Targeted material selection minimizes the risk of exploring undesired composition, e.g. minimizing hazardous chemical content (Principle 3 and 5). Machine learning combined with DoE helps to optimize newly discovered material to improve their performance, which seconds the efficiency principle (Principle 6).
The correlation between each one of the strategies presented and the Green Chemistry Principles is shown in Figure 1.
Summary of the green chemistry principles correlated to each one of the strategies presented.
Transition metal chalcogenides (TMC) constitute an important class of materials and include different types of oxides, sulfides, selenides, and tellurides. Increasing interest has been devoted to TMC due to their technological applications in different fields such as photocatalysis, sensors, solar cells, supercapacitors, electrocatalysis, heterogeneous catalysis, and many other applications [43]. In view of the growing need and interest for this class of materials, it is necessary to find ways to produce them through solution chemistry synthetic routes that minimize the environmental impacts and health-related risks. In order to demonstrate that it is possible to produce TMC by greener routes, throughout this section, we will show some successful examples where changes in the synthetic process yielded substantial improvements by decreasing environmental impacts and biological risks.
Quantum Dot is a class of oxides, sulfides, selenides, and tellurides with a particle size smaller than the Bohr radius for that certain material. Consequently, quantum dots are subjected to a phenomenon called quantum confinement, where their absorption and emission on UV–visible range happens at higher energy as the particle size decreases [44, 45].
Initially, the synthesis of quantum dots was established by Bawendi and co-workers, in 1993 [46]. They were able to prepare monodisperse quantum dots of CdS, CdSe, and CdTe, with controlled crystallite size between 1.2 and 11.5 nm. Two slightly different methods were used to produce these cadmium chalcogenides quantum dots. Both methods were based on the hot-injection of the organometallic Cd, and S, Se, or Te sources in a hot trioctylphosphine oxide (TOPO).
In the first method, the source of Cd was dimethyl cadmium (Me2Cd), and elemental Se and Te. Me2Cd was dissolved in trioctylphosphine (TOP), Se, and Te were also mixed with TOP to form the organometallic compounds trioctylphosphine selenide (TOPSe) and trioctylphosphine telluride (TOPTe). The source of Cd, plus TOPSe or TOPTe were injected at TOPO around 200°C, and growth proceeded for temperatures between 230 and 260°C.
In the second method, the solvent (TOPO) and source of Cd (Me2Cd and TOP) were kept, but the sources of S, Se, and Te were replaced, respectively, by bis(trimethylsilyl) sulfide ((TMS)2S), Bis(trimethylsilyl)selenium ((TMS)2Se), and Bis(tert-butyldimethylsilyl)tellurium ((BDMS)2Te). The growth temperature was between 290 and 320°C for larger particles, and around 100°C, for particles having size around 1.2 nm.
Since the pioneer paper by Bawendi et al. [46], this hot-injection method using TOP/TOPO as solvent has been used for the synthesis of many binary chalcogenides (ZnS [47], ZnS doped by Mn [48], ZnSe [49], CdTe [50]). However, there are many opportunities to make this hot-injection synthesis more environmentally friendly and less harmful. One point for improvement is that the solvents TOP and TOPO are both considered to cause severe skin burns and eye damage [51, 52]. The Me2Cd is a flammable and pyrophoric compound that can ignite spontaneously if exposed to air, release flammable gases when in contact with water, and cause severe skin burns and eye damage, and respiratory irritation [53]. Additionally, Peng
Looking at Green Chemistry perspective, this CdO route also represents an accomplishment of the principles number 3, 4, 5, 8, and 12. Also, the fact that it was necessary to add, at least one more reagent (HPA or TDPA) does not represent a disagreement with principle 2, since neither HPA nor TDPA presents any known health hazard or toxicity issue [56, 57].
The Greenest solvent possible would probably be water, and theoretically, any pair of ions that can generate an insoluble product would be enough to produce a binary TMC. For instance, to produce CdS, it would be necessary just to find out some sources of Cd2+ and S2−, and mix them in water in concentrations that would exceed the CdS solubility product for a certain temperature and pH condition. Although this strategy may work for certain cases, it is very likely to generate products having a broad size distribution, heterogeneous composition, and without any shape control. To avoid these drawbacks, it is necessary to use some capping agent. A completely Green synthesis of CdS was described by Unni
The aqueous based or hydrothermal syntheses of selenides and tellurides are more difficult to be carried out in comparison to the sulfides one, since it is hard to find non-toxic and stable sources of selenium and tellurium. For instance, both Na2Se and Na2Te are toxic and very reactive to be controlled during the reaction [5, 59, 60]. Reduction of elemental Se or Te by sodium borohydride (NaBH4) in an oxygen-free atmosphere has been often used as a way to deliver Se2− and Te2− in aqueous synthesis [60, 61]. However, if this reduction does not happen completely, in case of selenides, amorphous elemental selenium can be generated, and it can crystallize to trigonal selenium, even at room temperature, in a range of few months [62].
Sodium selenite (Na2SeO3) or tellurite (Na2TeO3) have good solubility in water and can be easily purchased. However, as the oxidation state of Se and Te is +4, respectively, on Na2SeO3 and Na2TeO3, it is necessary to use an effective reducing agent because Se or Te will need to be first reduced to oxidation state 0, then further reduced to oxidation state −2. The most common reducing agent used in this case has been hydrazine (N2H4). The hydrothermal synthesis of selenides and tellurides from Na2SeO3 or Na2TeO3, using N2H4 has been successful preparing many compounds like: CdSe and CdTe nanorods [63, 64], ZnSe nanoflowers [65], ZnTe nanowires [66], NiSe nanoparticles [67], NiTe nanorods [68], among many other transition metal selenides and tellurides [68, 69, 70]. Although hydrazine is a versatile solvent enabling the dissolution of many different cations at room temperature [71], its high toxicity, flammability, pyrophoricity, and carcinogenicity make it a solvent hard to handle safely and therefore unsuitable for large scale applications [72].
Aiming to find new ways to deliver Se2− and Te2− to make the aqueous synthesis of selenides and tellurides, Xia
Binary chalcogenides often contain cadmium as the metal cation, which is a toxic and carcinogenic element for humans [79, 80] and an environmental concern regarding its segregation and disposal [81]. Additionally, binary chalcogenides usually have band-gaps higher than 2.5 eV, which is suitable for the absorption of the UV radiation of the electromagnetic spectra, whereas ternary and quaternary chalcogenides typically have band-gaps ranging from 0.5 to 2.0 eV, which is suitable for the absorption of the visible light. Among different types of ternary and quaternary chalcogenides, we will focus our attention to a class of quaternary chalcogenide represented by the formula Cu2ZnSnS4, which will be hereafter referred to as CZTS. CZTS is comprised of non-toxic and earth-abundant elements, features that align with Green Chemistry Principles. In addition, its direct band-gap around 1.5 eV and high-absorption coefficient (> 104 cm−1) make CZTS a suitable material for many applications, including use as the absorber layer in thin film solar cells [82], counter-electrode material in dye sensitized solar cells [83], visible-light photocatalyst for water pollutant degradation [84], as well as use in heterostructures with Pt or Au for H2 evolution in water splitting systems [85].
Generally speaking, the successful hydrothermal synthesis of CZTS requires relatively long reaction times (ca. 12–24 h). To speed up the reaction and reduce the overall energy required, microwave heating represents a promising alternative to conventional heating. An important consideration is the capacity of a given solvent to absorb microwaves, which is measured by a parameter called dielectric loss factor (tan δ). In general, tan δ can be classified as high (tan δ >0.5), medium (0.1 < tan δ < 0.5), and low (tan δ <0.1) [86]. Water has a tan δ of 0.123, which is on the very low side of the medium range. Ethylene glycol, which does not have any toxicity if not ingested [87], has a high tan δ of 1.350 [86], which has led to its use for the solvothermal synthesis of CZTS using microwave heating.
Pinto
In both papers by Pinto
Table 2 summarizes the main papers presenting some advance towards the Green Chemistry application to the solution-based synthesis of transition metal chalcogenides nanoparticles.
Material | Size and morphology | Reactants | Method and Temperature | Reference |
---|---|---|---|---|
CdS | Quantum confined nanoparticles and nanorods | CdO, HPA, TDPA, TOPO, and S | Hot-injection at 300°C | [55] |
CdSe | Quantum confined nanoparticles and nanorods | CdO, HPA, TDPA, TOPO, and Se | Hot-injection at 300°C | [55] |
CdTe | Quantum confined nanoparticles and nanorods | CdO, HPA, TDPA, TOPO, and Te | Hot-injection at 300°C | [55] |
CdS | Quantum confined nanoparticles between 3 and 5 nm | CdSO4, Na2S2O3, water, thioglycerol | Co-precipitation at 30°C | [58] |
CdS:Zn+2 | Quantum confined nanoparticles between 3 and 5 nm | CdSO4, Na2S2O3, water, ZnSO4, thioglycerol | Co-precipitation at 30°C | [58] |
CdS:Cu+2 | Quantum confined nanoparticles between 3 and 5 nm | CdSO4, Na2S2O3, water, CuSO4, thioglycerol | Co-precipitation at 30°C | [58] |
CdTe/CdSe | Core-shell quantum dots | CdCl2, MPA, NaHTe, Na2SeSO3, water, | Reflux at 100°C to prepare the core CdTe, and around 78°C to prepare the shell CdSe | [73] |
Cu2ZnSnS4 (CZTS) | Agglomerated particles around 20 nm | Copper (I) acetate, Copper (II) acetate, zinc acetate, tin (II) acetate, tin (IV) acetate, Ethylene glycol, thiourea, L-cysteine, thioglycolic acid, MPA | Microwave heating at 160°C | [88] |
Cobalt-doped Cu2ZnSnS4 | Agglomerated particles around 20 nm | Copper (II) acetate, zinc acetate, tin (II) acetate, cobalt (II), ethylene glycol, thiourea, sodium thioglycolate | Microwave heating at 160°C | [89] |
Papers presenting some advance towards the green chemistry application to the solution-based synthesis of transition metal chalcogenides nanoparticles.
Generally speaking, the chemical synthesis of nanoparticles may involve the use of dangerous and non-biocompatible chemicals. This fact has created a demand for a greener approach to nanoparticles through biological synthesis.
Although the biological approach to quantum dots (QD) synthesis shows extreme promise with attributes such as control over the size and shape of the nanoparticles and biocompatibility, opening a market for the medical use of nanoparticles and TMCs. The promise of biologically synthesized nanoparticles fits right with the theme of
The biological approach typically uses cells within plants or fungi by surrounding the cells with a metal-ion solution that triggers a cell’s defense mechanism. These specific cells can be engineered biologically to produce different shaped QDs and TMCs.
Bacterial, microbial, and viral methods offer a promising future for QDs and TMC-nanoparticles in terms of ‘going greener’. The sheer variety of microorganisms provides a wide range of biological attributes from the natural selection from millions of years. As a result, these microorganisms are incredibly efficient at excreting the required enzymes to synthesize QDs and TMC-NPs both intra and extracellularly [90].
This synthesis method also generally occurs in relatively tame environments, bypassing the high heat and pressure usually associated with chemical synthesis. CdSe QDs, for example, typically require temperatures in the range of 240–300°C when chemically synthesized [91]. When performed biologically, the synthesis will occur close to room temperature, requiring less input work and falling right in line with Green Chemistry principles 6 and 12.
CdS QDs are other examples of a TMC that has high temperature and hazardous chemicals associated with it. A massive step forward in improving this process was the discovery of a genetically engineered strain of
Despite the additional steps necessary to obtain the CdS quantum dots, the intracellular preparation has proven to be capable of generating functional CdS quantum dots. For example, Yan and coworkers produced intracellularly from
On the other hand, many other forms of bacteria have been shown to excrete enzymes and synthesize QDs and nanocrystals extracellularly.
In general terms, control over the nanoparticles size and shape is also seen as a drawback in regard to bacteria and microbes because of the many variables to be controlled in the synthesis. That being said, differences in pH, metal-salt solution concentration, and temperature may all play a key role in dictating the size and shape of the nanoparticles. These conditions have also been found to dictate whether the synthesis occurs intra or extracellularly, both responding to different factors.
Few known species can reliably create specifically shaped nanostructures. For instance,
The variety of bacteria available in the environment and described in the literature could help us take advantage of the biomineralization processes similar to the ones demonstrated by
Fungi are part of another important category of microorganisms that can be used in the preparation of chalcogenide nanoparticles. For instance, Tudu and coworkers prepared spherical CdS QDs using the
The rot fungus
Table 3 summarizes the main papers some advance towards the Green Chemistry application of biological structures to the synthesis of transition metal chalcogenides nanoparticles.
Material | Size and morphology | Microorganism | Method and Temperature | Reference |
---|---|---|---|---|
CdS | Nanoparticles around 8 nm | Intracellular | [92] | |
CdS | Nanoparticles around 10 nm | Intracellular. | [93] | |
CdS | Particles ranging from 5 to 200 nm | Extracellular. Biotransformation of Cd+2 ions into | [94] | |
PbS | Hollow nanostructures and hollow nanotubes | Pb+2 ions and thioacetamide were dispersed in the microorganism suspension and sonicated at room temperature for 6 h | [95] | |
ZnS | Hollow nanostructures and hollow nanotubes | Zn+2 ions and thioacetamide were dispersed in the microorganism suspension and sonicated at room temperature for 6 h | [95] | |
CdS | Nanoparticles in the 3 to 5 nm range | Cd+2, microorganism extract, and Na2S were added in aqueous solution and heated at 60–80°C for 16 in the dark | [96] | |
CdS | Nanoparticles with size below 10 nm | Cd+2, MPA, thioacetamide, and the microorganisms were added to water, pH adjusted to 10. The mixture was incubated at 28°C for 24 h | [97] |
Papers presenting some advances towards the green chemistry application of biological organisms and structures for the synthesis of transition metal chalcogenides nanoparticles.
Mechanochemistry is the result of chemical transformations from grinding, milling, and similar changes in mechanical force, and this technique is often conducted without solvents [98].
Mechanochemistry has been more recently revived in part because of its alignment with the principles of
Mechanochemical syntheses with chalcogenides typically follow one of two routes in either a dry or wet mode [106]. In a dry reaction mode, metal and chalcogenide are milled together, while in wet systems, salts including acetates and sodium sulfide are used [107]. After completion, the reaction mixture is washed, and the sodium acetate or other salts are removed before drying. The reduction or elimination of solvent can have a cascade of impacts by increasing reaction efficiency and reducing costs related to waste disposal and treatment [108].
Mechanochemical methods have been leveraged to synthesize binary and complex metal sulfides and other chalcogenide nanoparticles [106, 109]. By controlling the mechanical energy of a system, chemists and engineers can tailor chemical and structural changes, polymorphic structures, and the materials resulting properties [101, 110, 111]. Short milling times on the order of seconds have yielded Cu3Se2 from copper selenide blends at room temperature [112].
Liquid-assisted milling systems have been demonstrated to run at low temperatures reducing energy consumption compared to reactions run at high temperatures [113]. Sonochemical methods represent different ways to promote and control reactions using sound energy in solutions where intense local heating and pressure events occur with short lifetimes [114, 115, 116]. These methods may be expanded to address how catalysts are used in fixed bed reactors in industry [117, 118].
The synthesis and use of metal chalcogenides present a green chemistry challenge from both the process safety and the environmental toxicology perspectives [119, 120]. Mechanochemical methods have been used to study the use of inert additives to synthesize copper sulfides in a non-explosive regime. This method allowed to control the obtained polymorph as covellite (CuS) or chalcocite (Cu2S) [121].
Ohtani
It is essential to highlight the additional use of complementary methods used alongside mechanochemical techniques such as thermal and microwave setups or electrical discharge milling (EDAMM) [106, 123]. All of these serve to extend the range and scope of transition metal chalcogenides and their engineered nanostructures possible to be obtained. Thus, providing accessible methods that spawn compelling functional products such as superconductive InXNb3Te4 [109].
In situ monitoring of mechanochemical reactions may improve methods for the real-time detections of polymorphs, products, and potential toxins, in line with the 12th principle of green chemistry. For more comprehensive information about the mechanochemistry scope on preparing TMCs, readers are welcome to check the review article by Baláž
The DoE can be applied in many situations involving TMCs preparation and performance. For instance, Ribeiro
However, in addition to these features, a 23 full factorial design followed by a central composite design (CCD) was carried out for each capping agent, which corresponds to eighteen experiments with four replicates in the center point. Three capping agents were studied, they were: 3-mercaptopropionic acid, L-glutathione, and 2-mercaptoethanesulfonate. The factors studied were the mole ratio between Cd2+ and Te2−, the mole ratio between Cd2+ and the capping agent, and the pH. The factorial design allowed them to obtain a quadratic model for the set of experiments for each one of the capping agents. Interestingly, the statistically significant terms vary according to the capping agent used. The response surface graphs obtained for each model revealed that the QY was maximized when the L-glutathione was used as the capping agent, in the following conditions: pH of 9.8, Cd2+/Te2− mole ratio of 1:0.2, and Cd2+/glutathione mole ratio of 1:0.77.
Copper antimony sulfides (Cu
Another promising alternative to the binary Cd or Pb-containing semiconductor quantum dots is the ternary semiconductor AgInS2. The AgInS2 has photoluminescence QY higher than 50%, all over the visible and near infrared range of the electromagnetic spectrum, when passivated with a ZnS shell [128]. With the goal to prepare AgInS2 based semiconductor quantum dots, Soares
Due to the large number of factors studied, the authors had to initially perform a fractional factorial design of the 25–1 type, which initially corresponded to 16 independent experiments. This initial design revealed that the significant factors were Ag:In ratio, MPA:In ratio, and the solution pH. Then, a 23 factorial design with central composite design was performed considering only the optimization of the three significant factors. The highest QY, which was around 0.46, was obtained for Ag:In equal to 0.1, pH 8.5, MPA:In equal 6. Besides the QY = 0.46, this set of conditions had an emission wavelength maximum at 625 nm, a lifetime emission of 400 ns, a Ag:In:Zn proportion about 1:3:5.
The application of semiconductor nanocrystals in solar cell devices requires the deposition of continuous thin films from the semiconductor materials. To obtain an appropriate thin film demands the optimization of several factors. In this sense, Ramírez
The examples presented in this section showed how the DoE could be used to decrease the number of experiments performed vastly in different contexts and stages of production of the TMCs semiconductor nanoparticles and thin films. Furthermore, the DoE provides statistical justification for each decision taken and for the empirical models developed, which can not be obtained through the OFAT approach.
As commented in Strategy 7 in Section 2.7, life cycle assessments (LCA) can contribute to developing a greener process by analyzing the resources and energy input, the cost-effectiveness, and the social and economic impacts involved in that specific process.
To illustrate the utility of life cycle assessments, a simplified LCA was performed by aggregating and reframing data from several different routes to distinctive copper sulfide nanosystems [22, 25]. Five different synthetic protocols were selected from the literature and with open-access resources these data are used to calculate metrics related to economic, societal, and environmental impacts associated to the chemicals and relative amounts in the protocols [131, 132, 133, 134, 135].
The selected synthetic protocols for copper sulfides share similarities. Most use different amounts of dodecanethiol as a source of sulfur and capping agent, along with solvents such as ethanol and chloroform for workup [131, 132, 133, 134, 135]. Despite common reagents, the methods and routes vary across the protocols allowing for access to downstream heterostructured nanoparticles [131], generalized procedures (towards an array of TMCs nanosystems) [132], a one-step synthesis and assembly [133], control over product shape [134], and solventless techniques [135]. These pieces of information from these five protocols were extracted from the literature and using physicochemical reference data [136, 137], SDS, and online databases [138]. The above metrics were calculated for the different sets of methods (Table 4).
Life Cycle Assessment of Copper Sulfide Nanosynthesis | ||||||
---|---|---|---|---|---|---|
Procedure | Process Mass Intensity | Material Cost | Global Warming Potential | Smog Formation Potential | Ingestion Toxicity | Product |
1 | 1.0 | 1.0 | — | 1.3 | 8.7 | Cu2S Hexagonal Nanoplatelets |
2 | 9.3 | 1.9 | — | 4.9 | 50 | Cu9S5 Digenite phase Nanowire Aggregates |
3 | 5.9 | 33 | 3.7 | 3.7 | 8.1 | Cu2S Tunable Nanoparticles |
4 | 1.3 | 8.4 | 1.0 | 1.0 | 1.2 | Cu2S 2D-Nanosheets |
5 | 6.8 | 49 | 6.0 | — | 1.0 | Cu2S Hexagonal Nanoplatelets |
Life cycle assessment of copper sulfide nanosystems. Metrics are normalized by column such that the least impact and most benign has a value of 1.
For this analysis, five metrics were selected to demonstrate the flexibility of this technique: process mass intensity, material cost, global warming potential, smog formation potential, and ingestion toxicity (Figure 2) [139, 140, 141]. While LCAs typically focus on environmental and energy impacts they may be broadened or adapted to address a variety of economic and societal impacts as with techno-economic assessments which have been performed for evaluating downstream copper recovery from spent electronics [20, 142, 143, 144]. An additional metric not used here which is relevant when screening metals with TMCs is the abiotic depletion potential, which examines the impacts of elemental scarcity in products including inorganic materials [26].
Metrics used to assess the synthesis of copper sulfide nanosystems and the information used to calculate these metrics.
Process mass intensity (PMI) is a ratio of the masses of the chemical inputs for a process over the products [139]. Similar to E-Factor and related to atom economy, process mass intensity accounts for byproducts and wastes, including solvents [145]. This may serve to help identify materially efficient transformations contributing to the prevention of waste [146]. Cost is determined as a sum of the scaled costs of each reagent used to synthesize 1 gram of product and does not include operational costs related to energy, nor capital costs for equipment, which are the focus of other LCAs [23, 27].
The final three metrics are related to human and environmental health. They are calculated using available potentials and safety data related to the specific impact of a given chemical in a procedure to a reference chemical such as toluene [141, 145, 147]. The potential for each chemical is multiplied by the relative mass per product and the sum of each potential generates an impact index. The relative magnitude of these indices identifies the expected impact of the procedure on a model environment [145]. Global warming potential (GWP) accounts for the incorporation of greenhouse gases, such as carbon dioxide, chloro- and hydrochlorofluorocarbons [17, 26]. Smog formation potential (SFP) assesses a variety of volatile organic chemicals such as ethanol and acetone, that can partake in reactions with pollutant oxidants at ground level [147, 148]. Ingestion toxicity (ING) models how chemicals may disperse in an external environment and their relative potential to harm living systems [21, 119].
Collecting these metrics, we can get a more holistic perspective of how a specific synthesis procedure can have both up- and down-stream impacts (Table 4). Here no one metric is any more important than another, and we quickly realize that there is no one best protocol. This allows us to look back at our work and examine why a specific value is high or low and why and how another procedure has a better or worse value and perhaps adopt a chemical or method from this protocol.
Templates and other resources for this type of LCA are available online with the full set of calculations available of this LCA in the supplementary information [23, 147, 149]. The reader should note the assumptions that accompany this analysis: primarily using a multi-compartmental model to represent an external environment, the standard estimation of amounts for techniques such as washing and filtering, and neglecting to consider the cost of water [141]. The functional unit for this assessment represents a way to standardize the analysis across the procedures for differently scaled references and here was set as 1 gram of product. After calculations are performed the data is normalized per metric such that the most benign or least impactful value is scaled to one.
Assessing a synthesis or comparing several goes beyond setting any one metric. Here one may make the distinction between the cost effective and efficient methods in the first procedure and the more costly, yes less environmentally impactful methods in the fourth protocol. Examining protocol one’s high ingestion toxicity parameter we can look back at the calculation to reveal it stems from a large excess of dodecanethiol. As this serves as solvent, a source of sulfur, and a capping agent, one may deem it a necessary hazard though they may also explore in the future how to better balance the stoichiometry of reagents.
Interestingly the solvent-free synthesis, method five, has a high process mass intensity and cost due to solvents such as chloroform that are used in the workup. LCA methods may be used to better model whether a system is greener than alternative methods by leveraging common and novel sustainability metrics [139, 140].
In contrast to the narrow focus on synthesis in the above example, LCAs ideally have a wide scope, including a look at sourcing reagents all the way to recovery or disposal [150]. A fundamental goal of green chemistry and systems thinking is the use of loops to close gaps between wastes and resources. LCAs may be used to address these considerations [151].
Although their power and flexibility LCAs are limited by the quality and accessibility of their source data. Even rudimentary assessments may prove inconclusive for certain values and metrics, note in Table 4 for dashed entries without a calculated value. Empty values in an LCA may indicate a lack of literature information or imperfect metric, which also provides researchers with a focus on what information is missing from the literature. As LCAs are inherently complex many choose to use proprietary software such as GaBi or ASPEN though researchers must be careful to avoid using LCAs as a black box. To help steer and assess the synthesis, application, scale-up, and recycling of transition metal chalcogenides life cycle assessments can be used to systematically evaluate procedures for green chemists and engineers.
Proposing new materials requires extensive sustainability, availability, and economic metrics analysis. While reports of individual compounds focus on specific composition metrics, typically considering economic factors. Only within the most recent years, in the extensive review work, it is important to compare the proposed class of compounds (chalcogenides in our case) with other classes, taking into account trends in economics.
In this section, we look at machine learning applied to the chalcogenide materials from three main aspects: First, we present a study showing how machine learning was used to catalog the band gap diversity among materials containing p-block elements. Second, we present and examples from the recent literature where machine learning was applied to predict properties and trends related to TMCs.
To estimate the fraction of chalcogenide materials among all compounds with report or calculated band gap or absence of the band gap, 6031 reports were analyzed. The band gap values obtained experimentally were summarized in the literature reports [152, 153, 154, 155]. For the compounds without band gaps, the data was extracted from the Materials Project database [156]. For this work, only a fraction of unique reports were used, and the full dataset is listed in the supporting information of the manuscript by Zhou
Absolute number of reports of materials with band gap and with metallic character.
A detailed analysis of the band gap reports for the Group VI elements, revealed that the element are distributed as the following: O—1014, S—1416, Se—1081, Te—614. Interestingly, the reported band gap value follows the trend of shifting the distribution of band gaps from insulating to metallic character for the elements of the Group VI, when going from lower principal quantum number to higher, similarly to the periodic table property change from non-metals to metals (Figure 4).
Band gap energy distribution for different oxides, sulfides, selenides, and telurides.
This is the most diverse distribution of the band gaps among all other p-block elements, allowing a band gap engineering for tailoring materials for a specific application need. From the average of 3.5 eV for oxygen 2.0 eV for S, 1.8 eV for Se, and 0.6 eV for Te, chalcogenide materials are the most suitable candidates for semiconductor synthesis and study.
The number of experimentally-confirmed predictions in the field of machine learning chemistry is limited [158]. Commonly, physics-based simulations (molecular dynamics or density functional theory) are regarded as experimental validation of a machine-learning model [159]. Developing a functional informatics infrastructure with training data pipeline, selection of appropriate algorithm, and assessment of model performance, requires expertise in both domain knowledge and informatics. This synergy is what converts model’s prediction to a real sample of a lab bench.
Combination for ML and DoE makes exploration of optimal synthesis conditions, faster and more efficient. For example, organic solar cell efficiency was increased substantially, increasing the efficiency of solar energy conversion from 6–8%, confirmed with a real working device [160]. Given that solar cell is also an area where chalcogenides are viewed as promising candidates, we can expect similar performance boost with application of machine learning methods [161].
Band gap prediction (fundamental for semiconductor, lighting, sensor, and solar cell areas) is one of the most common property prediction that was tackled with machine-learning methods [157, 162]. Chalcogenides, especially the binary ones with ZnS-type structure, are promising candidates. Transition metals (most common components in binary ZnS-type chalcogenides) are prone to statistical mixing, which allows for a precise control of the band structure in these materials [163]. Ternary chalcogenides were predicted to be promising p-type transparent conductors [162]. The compositional diversity of the chalcogenide-rich candidates is represented by two transition metal elements present (VCu3S4), or TM with p-block element (IrSbS), or alkaline-earth with metalloid (Ba2SiSe4). Double perovskites of chalcogenides are promising photovoltaics. The formation of perovskites is governed by strict structural geometrical rules along and charge-balanced composition. This puts limitation on the list of possible candidates, however, given that for double perovskites quaternary systems have to be explored, machine learning is essential, since the unexplored chemical white space in quaternary phase diagrams is impossible to explore experimentally [164].
This chapter presented many views and examples of how Green Chemistry can be used to benefit TMCs in different scientific fields. These benefits can be explored in many different stages of the TMCs planning and production. For instance, there are different approaches to make TMCs syntetic processes greener, such as using more benign reagents and solvents, milder synthetic conditions. Or using biological media or solventless methods like mechanochemistry.
Additionally, DoE can be used to plan more efficiently the number of experiments necessary to draw certain conclusions and obtain models allowing to predict initially untested synthetic conditions. LCA can be used to predict the risks, benefits, and environmental impacts involved in the production, use, and disposal of TMCs. Machine Learning is important in predicting TMCs properties, which offers useful guidelines for the synthesis of known TMCs. It is also valuable for predicting the structural features and properties of the materials never synthesized, opening up possibilities for discovering new TMCs.
We hope this chapter can be a resource for scientists aiming to make their nanoparticle synthetic processes more benign and environmentally friendly.
The authors would like to thank Manhattan College Department of Chemistry & Biochemistry and Manhattan College School of Science for their support.
The authors declare no conflict of interest.
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The chapter describes the design, implementation and integration of a ground penetrating radar (GPR) using a software defined radio (SDR) platform into the aerial drone. The chapter?s goal is first to tackle in detail the development of a custom-designed lightweight GPR by approaching interplay between hardware and software radio on an SDR platform. The SDR-based GPR system results on a much lighter sensing device compared against the conventional GPR systems found in the literature and with the capability of re-configuration in real-time for different landmines and terrains, with the capability of detecting landmines under terrains with different dielectric characteristics. Secondly, the chapter introduce the integration of the SDR-based GPR into an autonomous drone by describing the mechanical integration, communication system, the graphical user interface (GUI) together with the landmine detection and geo-mapping. This chapter approach completely the hardware and software implementation topics of the on-board GPR system given first a comprehensive background of the software-defined radar technology and second presenting the main features of the Tx and Rx modules. Additional details are presented related with the mechanical and functional integration of the GPR into the UAV system.",book:{id:"5905",slug:"robots-operating-in-hazardous-environments",title:"Robots Operating in Hazardous Environments",fullTitle:"Robots Operating in Hazardous Environments"},signatures:"Manuel Ricardo Pérez Cerquera, Julian David Colorado Montaño\nand Iván Mondragón",authors:[{id:"177422",title:"Dr.",name:"Julian",middleName:null,surname:"Colorado",slug:"julian-colorado",fullName:"Julian Colorado"},{id:"197884",title:"Prof.",name:"Ivan",middleName:null,surname:"Mondragon",slug:"ivan-mondragon",fullName:"Ivan Mondragon"},{id:"199958",title:"Prof.",name:"Manuel",middleName:null,surname:"Perez",slug:"manuel-perez",fullName:"Manuel Perez"}]},{id:"15855",title:"Kinematics of AdeptThree Robot Arm",slug:"kinematics-of-adeptthree-robot-arm",totalDownloads:14674,totalCrossrefCites:1,totalDimensionsCites:2,abstract:null,book:{id:"152",slug:"robot-arms",title:"Robot Arms",fullTitle:"Robot Arms"},signatures:"Adelhard Beni Rehiara",authors:[{id:"29287",title:"Dr.",name:"Adelhard",middleName:"Beni",surname:"Rehiara",slug:"adelhard-rehiara",fullName:"Adelhard Rehiara"}]},{id:"62978",title:"Intelligent Robotic Perception Systems",slug:"intelligent-robotic-perception-systems",totalDownloads:2445,totalCrossrefCites:5,totalDimensionsCites:11,abstract:"Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics.",book:{id:"7227",slug:"applications-of-mobile-robots",title:"Applications of Mobile Robots",fullTitle:"Applications of Mobile Robots"},signatures:"Cristiano Premebida, Rares Ambrus and Zoltan-Csaba Marton",authors:[{id:"203409",title:"Ph.D.",name:"Cristiano",middleName:null,surname:"Premebida",slug:"cristiano-premebida",fullName:"Cristiano Premebida"},{id:"254880",title:"Dr.",name:"Rares",middleName:null,surname:"Ambrus",slug:"rares-ambrus",fullName:"Rares Ambrus"},{id:"254881",title:"Dr.",name:"Zoltan-Csaba",middleName:null,surname:"Marton",slug:"zoltan-csaba-marton",fullName:"Zoltan-Csaba Marton"}]},{id:"67705",title:"Advanced UAVs Nonlinear Control Systems and Applications",slug:"advanced-uavs-nonlinear-control-systems-and-applications",totalDownloads:1976,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB®/Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results.",book:{id:"7792",slug:"unmanned-robotic-systems-and-applications",title:"Unmanned Robotic Systems and Applications",fullTitle:"Unmanned Robotic Systems and Applications"},signatures:"Abdulkader Joukhadar, Mohammad Alchehabi and Adnan Jejeh",authors:null}],onlineFirstChaptersFilter:{topicId:"22",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82223",title:"Biomechanical Design Principles Underpinning Anthropomorphic Manipulators",slug:"biomechanical-design-principles-underpinning-anthropomorphic-manipulators",totalDownloads:12,totalDimensionsCites:0,doi:"10.5772/intechopen.105434",abstract:"The biomechanical design of an artificial anthropomorphic manipulator is the focus of many researchers in diverse fields. Current electromechanical artificial hands are either in the research stage, expensive, have patents, lack severely in function, and/or are driven by robotic/mechanical principles, which tend to ignore the biological requirements of such designs. In response to the challenges addressed above this chapter discusses the potential of current technology and methods used in design to bridge the chasm that exists between robot manipulators and the human hand. This chapter elucidates artificial anthropomorphic manipulator design by outlining biomechanical concepts that contribute to the function, esthetics and performance of artificial manipulators. This chapter addresses joint stabilization, tendon structures and tendon excursion in artificial anthropomorphic manipulators.",book:{id:"11455",title:"Recent Advances in Robot Manipulators",coverURL:"https://cdn.intechopen.com/books/images_new/11455.jpg"},signatures:"Mahonri William Owen and Chikit Au"},{id:"82056",title:"Learning Robotic Ultrasound Skills from Human Demonstrations",slug:"learning-robotic-ultrasound-skills-from-human-demonstrations",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.105069",abstract:"Robotic ultrasound system plays a vital role in assisting or even replacing sonographers in some cases. However, modeling and learning ultrasound skills from professional sonographers are still challenging tasks that hinder the development of ultrasound systems’ autonomy. To solve these problems, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations1. First, ultrasound scanning skills are encapsulated into a high-dimensional multi-modal model, which takes ultrasound images, probe pose, and contact force into account. The model’s parameters can be learned from clinical ultrasound data demonstrated by professional sonographers. Second, the target function of autonomous ultrasound examinations is proposed, which can be solved roughly by the sampling-based strategy. The sonographers’ ultrasound skills can be represented by approximating the limit of the target function. Finally, the robustness of the proposed framework is validated with the experiments on ground-true data from sonographers.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Miao Li and Xutian Deng"},{id:"82057",title:"An Episodic-Procedural Semantic Memory Model for Continuous Topological Sensorimotor Map Building",slug:"an-episodic-procedural-semantic-memory-model-for-continuous-topological-sensorimotor-map-building",totalDownloads:8,totalDimensionsCites:0,doi:"10.5772/intechopen.104818",abstract:"For humans to understand the world around them, learning and memory are two cognitive processes of the human brain that are deeply connected. Memory allows information to retain and forms an experiences reservoir. Computational models replicating those memory attributes can lead to the practical use of robots in everyday human living environments. However, constantly acquiring environmental information in real-world, dynamic environments has remained a challenge for many years. This article proposes an episodic-procedure semantic memory model to continuously generate topological sensorimotor maps for robot navigation. The proposed model consists of two memory networks: i) episodic-procedural memory network (EPMN) and ii) semantic memory network (SMN). The EPMN comprises an Incremental Recurrent Kernel Machines (I-RKM) that clusters incoming input vectors as nodes and learns the activation patterns of the nodes for spatiotemporal encoding. The SMN then takes neuronal activity trajectories from the EPMN and task-relevant signals to update the SMN and produce more compact representations of episodic experience. Thus, both memory networks prevent catastrophic forgetting by constantly generating nodes when the network meets new inputs or updating node weights when the incoming input is similar to previously learned knowledge. In addition, idle or outlier nodes will be removed to preserve memory space.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Wei Hong Chin, Naoyuki Kubota and Chu Kiong Loo"},{id:"81922",title:"Skill Acquisition for Resource-Constrained Mobile Robots through Continuous Exploration",slug:"skill-acquisition-for-resource-constrained-mobile-robots-through-continuous-exploration",totalDownloads:18,totalDimensionsCites:0,doi:"10.5772/intechopen.104996",abstract:"We present a cognitive mobile robot that acquires knowledge, and autonomously learns higher-level abstract capabilities based on play instincts, inspired by human behavior. To this end, we (i) model skills, (ii) model the robot’s sensor and actuator space based on elementary physical properties, and (iii) propose algorithms inspired by humans’ play instincts that allow the robot to autonomously learn the skills based on its sensor and actuator capabilities. We model general knowledge in the form of competencies (skills) of the mobile robot based on kinematic properties using physical quantities. Thus, by design, our approach has the potential to cover very generic application domains. To connect desired skills to the primitive capabilities of the robot’s sensors and actuators, it playfully explores the effects of its actions on its sensory input, thus autonomously learning relations and dependencies and eventually the desired skill. KnowRob is used for knowledge representation and reasoning, and the robot’s operation is based on ROS. In the experiments, we use a millirobot, sized 2 cm2, equipped with two wheels, motion, and distance sensors. We show that our cognitive mobile robot can successfully and autonomously learn elementary motion skills based on a playful exploration of its wheels and sensors.",book:{id:"10823",title:"Cognitive Robotics",coverURL:"https://cdn.intechopen.com/books/images_new/10823.jpg"},signatures:"Markus D. Kobelrausch and Axel Jantsch"},{id:"81693",title:"The Neo-Mechanistic Model of Human Cognitive Computation and Its Major Challenges",slug:"the-neo-mechanistic-model-of-human-cognitive-computation-and-its-major-challenges",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.104995",abstract:"The neo-mechanistic theory of human cognition is currently one of the most accepted major theories in fields, such as cognitive science and cognitive neuroscience. This proposal offers an account of human cognitive computation, and it has been considered by its proponents as revolutionary and capable of integrating research concerning human cognition with new evidence provided by fields of biology and neuroscience. However, some complex cognitive capacities still present a challenge for explanations constructed by using this theoretical structure. In this chapter, I make a presentation of some of the central tenets of this framework and show in what dimensions it helps our understanding of human cognition concerning aspects of capacities, such as visual perception and memory consolidation. My central goal, however, is to show that to understand and explain some particular human cognitive capacities, such as self-consciousness and some conscious informal reasoning and decision making, the framework shows substantial limitations. 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Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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