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Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning

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Alexandre H. Pinto, Dylan R. Cho, Anton O. Oliynyk and Julian R. Silverman

Submitted: February 9th, 2022Reviewed: March 9th, 2022Published: May 13th, 2022

DOI: 10.5772/intechopen.104432

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Green Chemistry - New PerspectivesEdited by Brajesh Kumar

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Green Chemistry - New Perspectives [Working Title]

Dr. Brajesh Kumar and Dr. Alexis Debut

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Abstract

Transition metal chalcogenides (TMC) is a broad class of materials comprising binary, ternary, quaternary, and multinary oxides, sulfides, selenides, and tellurides. These materials have application in different areas such as solar cells, photocatalysis, sensors, photoinduced therapy, and fluorescent labeling. Due to the technological importance of this class of material, it is necessary to find synthetic methods to produce them through procedures aligned with the Green Chemistry. In this sense, this chapter presents opportunities to make the solution chemistry synthesis of TMC greener. In addition to synthesis, the chapter presents different techniques of experimental planning and analysis, such as design of experiments, life cycle assessment, and machine learning. Then, it explains how Green Chemistry can benefit from each one of these techniques, and how they are related to the Green Chemistry Principles. Focus is placed on binary chalcogenides (sulfides, selenides, and tellurides), and the quaternary sulfide Cu2ZnSnS4 (CZTS), due to its application in many fields like solar energy, photocatalysis, and water splitting. The Green Chemistry synthesis, characterization, and application of these materials may represent sustainable and effective ways to save energy and resources without compromising the quality of the produced material.

Keywords

  • transition metal chalcogenides
  • green chemistry
  • synthesis
  • design of experiments
  • life cycle assessment
  • machine learning

1. Introduction

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 12 Principles of Green Chemistry[3]. These Principles serve as guidelines for good practices regarding minimization of chemical waste production, mitigation of harmful or hard to treat byproducts, atom economy, development of materials with reasonable degradation period after the end of their lifecycles, and search for safer chemical sources, renewable feedstocks, and energy-efficient processes.

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 et al.[4] published a paper presenting a comparison between the materials context and the molecular context, which were adapted and presented in Table 1.

ConceptMolecular ContextMaterials Context
CompositionDefined by molecular formulaCore and surface composition difficult to define; may vary according to sample shape and size
Size/shapeDefined molecular structure and shapeOften a mixture of sizes and shapes, dependent on synthetic method
DispersitySingle and continuous composition and structureCharacterized by distributions of composition and structural features
PurityPurification procedure is intimately related to molecular structure (i.e. chromatography)Small molecule impurities coming from surface coating or unreacted precursors significantly influence properties
ToxicityPossible to assess based on molecular structureIt may be inherent to the composition but also can be related to particle size, shape, or surface coating

Table 1.

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.

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2. Strategies to make a nanoparticle synthetic process greener

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 green upthe planning, preparation, and analysis of transition metal chalcogenides alongside the Green Chemistry Principles addressed by making the change [4, 9].

2.1 Strategy 1: safer reagents and solvents

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.

2.2 Strategy 2: use more efficient energy input sources

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.

2.3 Strategy 3: eliminate or minimize byproducts

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.

2.4 Strategy 4: avoid using unnecessary additives and steps

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.

2.5 Strategy 5: greener purification procedures

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 green up.

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.

2.6 Strategy 6: the use of design of experiments

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 2kfull factorial design, where the number two relates to the number of levels, in this case, two for each factor. And the exponent krelates to the number of factors, in this example k = 3, due to the factors temperature, concentration, or pH. Therefore, this example explains a 23 full factorial design, and the result of the calculation that names the factorial design is equal to the number of independent experiments necessary to complete the factorial design [16]. This is the reason why eight independent experiments were required to complete the factorial design from this example.

2.7 Strategy 7: The use of life cycle assessment (LCA)

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 [2425]. 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.

2.8 Strategy 8: the role of machine learning in predicting materials properties

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, e.g.,synthesis conditions and crystal structures or composition and property; (ii) optimization of synthesis conditions; (ii) prediction of candidates with desired properties. In short, machine learning allows fast and detailed analysis of the available data to provide a list of potential candidates, which we can synthesize with fewer experiments.

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.

Figure 1.

Summary of the green chemistry principles correlated to each one of the strategies presented.

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3. Applications of green chemistry principles in the synthesis of transition metal chalcogenides

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.

3.1 Solution-based synthesis of transition metal chalcogenides nanoparticles

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 et al.[54] reported that Me2Cd supposedly decomposes generating some insoluble metallic cadmium when in contact with TOPO. To overcome all these problems related to the use of Me2Cd, Peng’s group developed a greener version of the hot-injection route in TOP/TOPO, where they replaced the Me2Cd by cadmium oxide (CdO) [55]. Going more in depth, the CdO was solubilized by forming a complex with hexylphosphonic acid (HPA) or tetradecylphosphonic acid (TDPA) at temperatures around 300°C after injection in TOPO. This route enabled the production of monodisperse quantum dots of CdSe, CdS, and CdTe [55]. It also allowed a remarkable shape control, since it was able to produce CdSe quantum dots and rods, just by varying the precursors concentration and growth time and temperature [54].

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 et al., [58] where they used CdSO4 and Na2S2O3.5H2O, as cadmium and sulfur sources, and thioglycerol as capping agent. The reagents were mixed in water at 30°C and stirred at the same temperature for 1 hour, producing monodisperse quantum-confined CdS, having crystallite size around 3–5 nm. The photoluminescence (PL) of these CdS quantum dots could be controlled by successfully doping them with Cu2+ (PL red-shifted) or Zn2+ (PL blue-shifted) [58]. The use of unharmful solvents and room temperature synthesis complies with Green Chemistry Principles 3, 5, 6, and 12.

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 et al.[73] prepared CdTe/CdSe core-shell quantum dots in water using oxygen-free NaHTe and Na2SeSO3 as sulfur sources, and mercaptopropionic acid (MPA) as capping agent. NaHTe can easily be prepared by heating elemental selenium in ethanol with excess NaBH4, in oxygen-free atmosphere [74] also no toxicity information was found for NaHTe. The aqueous route using NaHTe to prepare CdTe proved to be very versatile regarding the capacity to produce nanomaterials from many different shapes, sizes, and PL emission, just by varying pH, counterion on Cd2+ source, the thiol capping agent, and the mole ratio between cadmium and tellurium source [75]. The route is adaptable regarding the variety of binary TMC able to be prepared, such as ZnTe [76], Ag2Te [77], and PbTe [78].

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 et al.developed a microwave-assisted method using ethylene glycol as the solvent to prepare CZTS nanoparticles by controlling the percentage of the kesterite and wurtzite crystal phases by varying the amount of thiourea, and the initial oxidation state of the tin acetate (between Sn+2 and Sn+4) used as the tin source [88]. Later, the same group prepared CZTS doped with Co+2 ions (Cu2(Zn1-xCox)SnS4) using a similar synthetic microwave-assisted route, having ethylene glycol as solvent. These powders were dispersed in water and deposited in molybdenum-coated substrates, followed by annealing at 600°C producing dense Cu2(Zn1-xCox)SnS4 thin films [89].

In both papers by Pinto et al., the microwave heating procedure was carried out at 160°C for 20 minutes, which is usually much shorter than the time used in the regular solvothermal heating procedures [88, 89].

Table 2 summarizes the main papers presenting some advance towards the Green Chemistry application to the solution-based synthesis of transition metal chalcogenides nanoparticles.

MaterialSize and morphologyReactantsMethod and TemperatureReference
CdSQuantum confined nanoparticles and nanorodsCdO, HPA, TDPA, TOPO, and SHot-injection at 300°C[55]
CdSeQuantum confined nanoparticles and nanorodsCdO, HPA, TDPA, TOPO, and SeHot-injection at 300°C[55]
CdTeQuantum confined nanoparticles and nanorodsCdO, HPA, TDPA, TOPO, and TeHot-injection at 300°C[55]
CdSQuantum confined nanoparticles between 3 and 5 nmCdSO4, Na2S2O3, water, thioglycerolCo-precipitation at 30°C[58]
CdS:Zn+2Quantum confined nanoparticles between 3 and 5 nmCdSO4, Na2S2O3, water, ZnSO4, thioglycerolCo-precipitation at 30°C[58]
CdS:Cu+2Quantum confined nanoparticles between 3 and 5 nmCdSO4, Na2S2O3, water, CuSO4, thioglycerolCo-precipitation at 30°C[58]
CdTe/CdSeCore-shell quantum dotsCdCl2, 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 nmCopper (I) acetate, Copper (II) acetate, zinc acetate, tin (II) acetate, tin (IV) acetate, Ethylene glycol, thiourea, L-cysteine, thioglycolic acid, MPAMicrowave heating at 160°C[88]
Cobalt-doped Cu2ZnSnS4Agglomerated particles around 20 nmCopper (II) acetate, zinc acetate, tin (II) acetate, cobalt (II), ethylene glycol, thiourea, sodium thioglycolateMicrowave heating at 160°C[89]

Table 2.

Papers presenting some advance towards the green chemistry application to the solution-based synthesis of transition metal chalcogenides nanoparticles.

3.2 Biological approaches for the synthesis of transition metal chalcogenides

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 Green Chemistryprinciples 1, 2, 3, 4, 8, 10, 11, and 12.

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 Escherichia coliwas shown to synthesize CdS QDs intracellularly[92]. Intracellular synthesis is generally considered a shortcoming on the bacterial and microbial front of biosynthesis. When QDs are synthesized intracellularly, the steps required to harvest said QDs often proves to negate the benefits of biosynthesis in the first place. To separate the QDs from the E. colicells, the cells were lysed with a hyper acoustic cell grinder and centrifuged. The re-suspended cells were then freeze-thawed at −70° C. The QDs then had to be purified using anion exchange resin columns [92]. This energy and time-consuming process is one of many associated with intracellular synthesis and thus may diminish the purpose of a greener synthesis.

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 E. colicells CdS QDs with a fluorescent emission at 470 nm, when excited by UV radiation. Additionally, antimicrobial susceptibility studies showed that the resistance of E. colicells to eight different antibiotics is minimally changed when comparing before and after the CdS production [93].

On the other hand, many other forms of bacteria have been shown to excrete enzymes and synthesize QDs and nanocrystals extracellularly. Klebsiella pneumoniaegenerated CdS quantum particles extracellularly [94]. The bacteria were placed in a nutrient-rich broth that acted as a source for sulfate ions. The sulfate ions were absorbed by the microbe and converted to adenosine phosphosulfate with the ATP sulfurylase secreted by the microbe. The created adenosine phosphosulfate was then phosphorylated into 3’phosphoadinosine phosphosulfate. Then, with the help of another enzyme provided by the microbe (phosphoadinosine phosphosulfate reductase), the 3’phosphoadinosine phosphosulfate was reduced into sulfite, which was further reduced to sulfide [94]. These sulfide ions could then react with metal ions like Cd2+ to form CdS NC extracellularly [94]. This biological method comes with inherent advantages that bypass intracellular synthesis drawbacks, using much less energy in the process.

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, Streptococcus thermophilus (Str. Thermophilus)is known to produce ZnS and PbS hollow nano-spheres through a sonochemical process. During the respective processes, the ZnS and PbS nanoparticles cluster on the surface of the cell walls of the bacteria. The bacteria can then be removed using sonication, leaving just the hollow nanostructures built up by the cell walls [95].

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 Str. Thermophilus.

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 Termitomyces heimiifungus extracted from mushrooms [96]. The synthesis was carried out in water, using Cd(NO3)2 and Na2S as Cd2+ and S2− sources, respectively. Then, the mushroom extract was added to the reaction mixture, which was further heated up at 60 to 80°C for 16 hours. Increasing the extract volume added led to a decrease in the particle size accompanied by an increase in the band gap energy, which varied between 2.5 and 2.8 eV. The infrared spectroscopy results revealed the presence of bands related to Cd-S bonds and also of proteins from the Termitomyces heimiifungus [96].

The rot fungus Trametes versicoloris another example of fungus used in the CdS preparation [97]. The fungus mycelium was added to an aqueous mixture of Cd(NO3)2.4H2O, thioacetamide, and mercaptoacetic acid. The reaction mixture pH was raised to 10, and the mixture was shaken at 28°C for 24 hours. In the end of the process, spherical nanoparticles, with an average diameter of 6 nm were formed, according to TEM results. The XRD confirmed the presence of the CdS cubic phase. Similar to other works using fungal biosynthesis, the nanoparticle surface indicated the presence of fungal proteins. The authors hypothesized that the fungal proteins attached to the nanoparticle surface due to a defense mechanism of the mycelium from the Cd2+ presence. Then, these proteins chelated to Cd2+, which further reacted with S2− ions, producing the CdS nuclei [97].

Table 3 summarizes the main papers some advance towards the Green Chemistry application of biological structures to the synthesis of transition metal chalcogenides nanoparticles.

MaterialSize and morphologyMicroorganismMethod and TemperatureReference
CdSNanoparticles around 8 nmE. coliIntracellular. E. colicells incubation and genetic expression at room temperature[92]
CdSNanoparticles around 10 nmE. coliIntracellular. E. coliincubation with CdCl2 in the dark at 37°C for different days[93]
CdSParticles ranging from 5 to 200 nmKlebsiella pneumoniaeExtracellular. Biotransformation of Cd+2 ions into K. pneumoniaecells surface[94]
PbSHollow nanostructures and hollow nanotubesStreptococcus thermophilus(for nanospheres) and L. bulgaricus(for nanotubes)Pb+2 ions and thioacetamide were dispersed in the microorganism suspension and sonicated at room temperature for 6 h[95]
ZnSHollow nanostructures and hollow nanotubesS. thermophilus(for nanospheres) and Lactobacillus acidophilus(for nanotubes)Zn+2 ions and thioacetamide were dispersed in the microorganism suspension and sonicated at room temperature for 6 h[95]
CdSNanoparticles in the 3 to 5 nm rangeTermitomyces heimiiCd+2, microorganism extract, and Na2S were added in aqueous solution and heated at 60–80°C for 16 in the dark[96]
CdSNanoparticles with size below 10 nmTrametes versicolorCd+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]

Table 3.

Papers presenting some advances towards the green chemistry application of biological organisms and structures for the synthesis of transition metal chalcogenides nanoparticles.

3.3 Mechanochemistry synthesis of transition metal chalcogenides

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 Green Chemistry[99, 100, 101, 102]. A typical mechanochemistry application is the use of advanced milling methods, including high-energy and ‘online’ or real-time analyses. These methods allow for the assessment, and stepwise formation of specific nanoparticle materials and metal organic framework (MOF) polymorphs inaccessible through solution-based techniques [103, 104, 105]. Mechanochemistry also has demonstrated to perform various catalytic transformations making it an attractive alternative for carefully controlling the stoichiometry of nanoparticle products and their activity [101].

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 et al. have used mechanical alloying which is a solid-state technique performed with a high energy planetary ball mill to produce homogenous powder silver, samarium, or copper-based sulfides, selenides, or tellurides, allowing to control the stoichiometry and polymorph obtained [112, 122].

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 et al. [106].

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4. Applications of design of experiments for transition metal chalcogenides

The DoE can be applied in many situations involving TMCs preparation and performance. For instance, Ribeiro et al. used DoE of experiments to prepare CdTe quantum dots aiming to maximize the photoluminescence quantum yield (QY) [124]. These quantum dots were prepared in water, using microwaves as the heating method, fixing the temperature at 100°C for 30 minutes. The water as the solvent, the relatively low temperature, and the short reaction time are features that would be enough to consider this method as green.

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 (CuxSbySz) are a promising alternative for toxic chalcogenides containing cadmium or lead and for the indium-based semiconductors like CuInSe2 due to the indium scarcity and consequent high price [125]. The CuxSbySzhas four main polymorphs chalcostibite (CuSbS2), tetrahedrite (Cu12Sb4S13), skinnerite (Cu3SbS3), and famatinite (Cu3SbS4) [126]. To better understand the formation of each one of these phases, Pretto et al.performed a 24 factorial design to study the influence of the time (1 or 5 minutes), temperature (200 or 250°C), type of solvent (oleylamine or a mixture oleylamine and diphenyl ether), and the heating method (hot injection or heat up) [127]. The 24 factorial design required 16 independent experiments. The factorial design revealed that the heat up method led to the CuSbS2 formation, whereas the hot injection led to the formation of Cu3SbS4 phase. The temperature as high as 250°C also led to the selective formation of CuSbS2.

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 et al. prepared AgInS2/ZnS capped by mercaptopropionic acid (MPA) [129]. The factors studied were the reaction time, temperature, Ag:In ratio, S:In ratio, Zn:In ratio, MPA:In ratio, and pH of the solution. The response factors studied were the PL maximum wavelength, the PL lifetime, and QY.

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 et al. studied CZTS thin films’ deposition by spray pyrolysis, starting from a mixture of the Cu2+, Zn2+, and Sn4+ salts in a mixture of acetone and DMSO [130]. The factors studied in this deposition process were the substrate temperature (350 and 450°C), the carrier gas pressure (2 and 4 bar), and the spray pulse time (0.4 to 1.2 s). The response factor studied was the film resistivity, which should be minimized. By performing a 23 factorial design with a face-centered central composite design, obtaining an empirical equation quadratic for the substrate temperature, linear for the other terms, and considering significant the interaction between the substrate temperature and pulse time. The lowest value for resistivity was obtained for films deposited at 400°C, carrier gas pressure lower than 0.3 bar, and spray pulse time lower than 0.8 s. For those conditions, the resistivity values between 10 to 52 Ω were obtained.

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.

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5. Applications of life cycle assessment for transition metal chalcogenides

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
ProcedureProcess Mass IntensityMaterial CostGlobal Warming PotentialSmog Formation PotentialIngestion ToxicityProduct
11.01.01.38.7Cu2S Hexagonal Nanoplatelets
29.31.94.950Cu9S5 Digenite phase Nanowire Aggregates
35.9333.73.78.1Cu2S Tunable Nanoparticles
41.38.41.01.01.2Cu2S 2D-Nanosheets
56.8496.01.0Cu2S Hexagonal Nanoplatelets

Table 4.

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].

Figure 2.

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.

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6. Applications of machine learning for transition metal chalcogenides

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.

6.1 Band gap diversity among materials containing p-block elements

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 et al. [157]. Out of 6031 reports, the compounds with elements from Group VII—452, Group VI—3801, Group V—1905, Group IV—1441, and Group III—1676. Out of all p-block elements, Group VI (O, S, Se, Te), mainly consisted of chalcogenides is the most frequently reported (Figure 3).

Figure 3.

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).

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.

6.2 Property prediction using machine learning for transition metal chalcogenides

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].

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7. Conclusions

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.

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Acknowledgments

The authors would like to thank Manhattan College Department of Chemistry & Biochemistry and Manhattan College School of Science for their support.

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Conflict of interest

The authors declare no conflict of interest.

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Written By

Alexandre H. Pinto, Dylan R. Cho, Anton O. Oliynyk and Julian R. Silverman

Submitted: February 9th, 2022Reviewed: March 9th, 2022Published: May 13th, 2022