Welding processes group.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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It is used in simple structures fabrication, nuclear and petroleum industries, as well as chemical components.
In a typical fusion welding process of metals, such as resistance welding, arc welding, electron beam welding, laser welding, a heat source is applied locally to the interfaces of the two metals to be joined. The interface can be metals’ surfaces, where faces of each other are joined by a nugget (e.g., spot or resistance welding). In arc welding, the interface will be the weld seam. However, complex physical phenomena and processes occur due to the heating/melting and cooling/solidifying. This may produce adverse effects on weld properties and base metal properties [2]. In order to reduce adverse effects and obtain desired results, many studies have been developed to monitor, predict, or control welding processes. All these studies are based on the optimal welding parameters’ adjustment, but all of these are adjustable.
All adjustable welding parameters, such as current or current waveform, heat input, wire feed speed, travel speed, and arc voltage, may be used as system inputs and be designed to assure the required outputs. For that reason and the interrelations-parameters complexity, welding process can be analyzed like a stochastic system, which has input and output parameters and several disturbances [2]. Chen’s article [3] was related to the need to improve the information acquired from these welding parameters and identify characteristics in order to improve and control the welding process results. Chen defined new objectives of modern welding manufacturing technology to show the way for better welding processes. It exposes some problems of the intelligentized welding manufacturing technology (IWMT), which are shown in Figure 1.
Some technical problems in IWMT [
Other science areas present the potential to solve these problems. Computer science areas have had great results with new technique applications of data analysis, learning models, and intelligent control. Data analysis objective indicates nontrivial features on a large amount of data. Due to the increase and complexity of data, more efficient data analysis techniques have been developed. Welding process can be analyzed with this point of view. So, the welding process analysis with new techniques is nothing more than a continuity in the development of welding analysis processes. This interdisciplinarity is one of the necessary contributions proclaimed by the so-called Industry 4.0, like the one shown in [4, 5, 6].
The fourth industrial revolution refers to the next manufacturing generation, where automation technology will be improved by self-optimization and intelligent feedback [7]. For this reason, the application of the most recent data analysis techniques and processes can contribute to a better control and monitoring of welding processes. These techniques can be joined in machine learning techniques [8, 9, 10, 11, 12], data mining process [13, 14, 15, 16, 17], and control process [18, 19, 20]. The interrelation of these areas and their origins are presented in Figure 2. Machine learning is a growing area in computer science, with far-reaching applications, for data analysis [21]. Machine learning uses computer theory and statistics for building mathematical models with the goal of making inference from a sample [22]. One branch in machine learning with fast growing is deep learning. These methods are an essential part of the research on speech recognition in the state of the art [23], image recognition [24, 25, 26], object detection [27, 28], videos [29, 30], and sound [31, 32] analysis. Interesting patterns come out from such a machine learning techniques. One important process is data mining. Data mining puts strong emphasis on different aspects, like efficiency, effectiveness, and validity of process [33]. Data mining processes define several stages and methodologies to achieve these objectives, as exposed by Marbán in [34]. An important objective of data analysis is to reveal and indicate diverse, nontrivial features in data. For this reason, welding process can be analyzed with this point of view.
Origin diagram of the new data analysis techniques.
A search conducted in the Web of Science from 2011 to October 3, 2018, shows the growing trend of these new data analysis techniques and processes in welding process researches Figure 3, but when comparing with the investigations on models welding process, growth is almost imperceptible, as appearing in Figure 4.
Cited per year on welding (Web of Science [
Cited per year on welding (Web of Science [
These demonstrate the need for this review to show these techniques, the advantages in their applications, and the increasing trend of their utilization. This review can be resumed in following stages:
Welding process—understanding of welding processes being analyzed.
Sensors—analysis of some principal sensors in welding process.
Data processing—analysis of technique to transform sensors information to welding process dataset.
Modeling welding process—analysis of some modeling techniques in welding process.
Intelligent control of welding process—analysis of some intelligent control techniques in welding process.
These stages has a close relationship with data mining processes as a sample [34].
American Welding Society (AWS) definition for a welding process is:
“
AWS defines groups of welding techniques depending on the energy transfer mode. The processes analyzed in this chapter are grouped as shown in Table 1.
Group | Welding process |
---|---|
Arc welding | Gas metal arc welding (GMAW) |
Gas tungsten arc welding (GTAW) | |
Plasma arc welding (PAW) | |
Shielded metal arc welding (SMAW) | |
Submerged arc welding (SAW) | |
Variable polarity plasma arc welding (VPPAW) | |
Rotating arc narrow gap MAG welding (RANGMW) | |
Girth welds | |
Resistance welding | Resistance spot welding (RSW) |
Large scale RSW (LSRSW) | |
Other welding processes | Laser beam welding (LBW) |
Welding processes group.
These groups present different parameters and characteristics that were analyzed in the articles presented in this chapter.
The group arc welding is characterized with electric arc. The electric arc is the heat source most commonly used in fusion welding of metallic materials. The welding arc comprises a relatively small region of space characterized by high temperatures (similar to or even higher than the sun’s surface), strong generation of light and ultraviolet radiation, intense flow of matter, and large gradients of physical properties. It has an adequate concentration of energy for localized base metal fusion, ease of control, low relative cost of equipment, and an acceptable level of health risks to its operators. The study of the arc is of special interest in areas such as astrophysics and the electrical and nuclear industries [37]. The electric arc generates a complex interrelation of thermal, electrical, and magnetic parameters. These are hampering much of their studies based on definite theoretical formulations. Despite many studies, the electric arc is quite complex and the knowledge so far allows a partial understanding of the phenomenon [1].
Resistance welding is the joining of metals by applying pressure and passing current for a length of time through the metal area that is to be joined. Its principal advantage is no other materials are needed to create the bond; this reason makes this process extremely cost effective. Resistance welding is applied in a wide range of automotive, aerospace, and industrial applications. Among the main parameters are welding time, welding force, contact resistance, materials properties [1]. Resistance spot welding, like all resistance welding processes, creates welds using heat generated by resistance to the flow of welding current between the faying surfaces, as well as force to push the workpieces together, applied over a defined period of time. Resistance spot welding uses the electrode face geometries to focus the welding current at the desired location, and apply force to the workpieces. Once sufficient resistance is generated, the materials set down and combine, and a weld nugget is formed [36]. The process is fast and effective, and it is also complicated due to complex interactions between electrical, mechanical, thermal, and metallurgical processes. The heat generation in RSW is due to the resistance of the parts being welded to the flow of a localized electric current, based on Joule’s law. The quality of the joint in RSW is influenced by the welding parameters. These parameters mainly include welding current, welding time, electrode force, and electrode geometry [38]. Large scale resistance spot welding (LSRSW), as mentioned in Table 1, is generally adopted in the automotive industry. It is an automotive structure that includes thousands of spot welds. It presents the same parameters and complexity as RSW; only the parameters related and influenced by its scalability are increased [39].
In this group, AWS presents various welding processes. Laser welding is the only one belonging to this group, which is found in the analyzed articles.
Laser beam welding is one of the most technically advanced welding processes. Laser welding is in general a keyhole fusion welding technique which is achieved with the very high power density obtained by focusing a beam of laser light to a very fine spot [40]. This light ray heats metals up quickly so that the two pieces fuse together into one unit. The light beam is very small and focused, so the metal weld also cools very quickly. Laser welding operates in two fundamentally different modes: conduction limited welding and keyhole welding. The mode in which the laser beam will interact with the material is welding; it will depend on the power density of the focused laser spot on the work piece [41].
Other parameters that are present in these processes are those of final welding geometry, which behave differently in different processes and under different conditions. The parameters of the respective sources generate their influence on the final result of each welding process.
Welding is a complex process, so it requires more intelligent techniques in its analysis, monitoring, and production quality improvement. The use of sensors allows the acquisition of process parameters. The new artificial intelligence techniques will allow a better study, modeling, and control of these processes.
Several sensors have been applied in the welding process for monitoring. The weld bead and the weld-pool indirect sensing technologies can be classified like exposed in [42] and in Figure 5.
Some indirect monitoring technologies in welding process [
Infrared vision techniques have been widely applied in the welding process [43, 44, 45, 46, 47, 48, 49, 50]. One of the problems of this technique is that the environment where it is applied can interfere in the precision of the data obtained from a process. This may be due to the own heat emission of the technologies utilized.
Sound may indicate conditions that generate weld defects. Acoustic information plays a relevant role for expert welders, as described in [51]. Sound signature produced by GMAW contains information about arc column behavior, the molten metal, and the metal transfer mode. High-speed data acquisition and computer-aided analysis of sound signature may indicate conditions that generate weld defects [52, 53]. Di Wu, in 2016 [54], tried to monitor penetration and keyhole with acoustic signals and image analysis. Lv et al. [55], proposed a recognition model to analyze the relationship between penetration state and arc sound. In 2017, Lv et al. [56] again presented a welding quality control in pulse gas tungsten arc welding (P-GTAW). The welding acoustic signal was used to analyze the design of an automated welding penetration control system.
In welding, it is easy to capture sound, but it is very difficult to analyze the noises and differences of intensities that are sometimes generated. This is not a problem to sound deep learning technique like present [32, 57]. To understand the welding sound analysis with deep learning techniques, it is necessary make an image arc correlation to know what happens in welding arc.
Vision sensor is largely utilized in welding process to analyze weld-pool process [58, 59], arc-welding process [60, 61], and weld bead geometry [62, 63]. The more light generated by arc can be difficult for the image obtention. Some techniques are utilized. One of them was utilized by Chen in 2010 [64].
He made monitoring and control of the hybrid laser-gas metal arc welding process with an economical sensor system, and a coaxial vision system, which was integrated from a relatively inexpensive industrial vision system and a personal computer (PC). Another visualization technique is Shadowgraphy, applied in Esdras Ramos investigation, in 2013 [65, 66]. This is based on process shadow arc with laser source.
In [60], a laser illumination was utilized. To reduce the arc light, a narrow band interference filter was applied. For precise measurements, an image-analysis technique was used. This technique can be used to obtain high quality images but only it can be used in processes without material transfer.
Chen et al. [67] utilized a visual double-sided sensing system. In one frame, the weld-pool geometry parameters in GTAW process were determined.
With high speed illumination laser in [68], great quality images are obtained. This technique is more recent one but it needs a laser with more potentiality than Shadowgraphy technique. This technique is more expensive too.
Some papers define their own image processing technologies, like Hong Yue in 2009 [69], where the weld image processing adopts the classic techniques such as Laplacian, Gaussian, neighborhood mean filters, and threshold segmentation. Yanling Xu, in 2014 [70], proposed the Canny edge detection algorithm for detecting edges and extracting pool and seam characteristic parameters. Qian-Qian Wu in [71] researched to find out the optimal algorithm to filter. He made a comparison of Wiener filter, Gaussian filter, and Median filter on welding seam image. In the classic image processing, it is very difficult to generalize a filter or algorithm, because it depends on the conditions and characteristics of camera parameters and light.
Another problem with these algorithms mentioned above is that the real-time analysis has an insufficient response time to be utilized in a process control despite recent developments in computational resources.
Deep learning techniques have efficient result in real-time executions [28] and classifications [24, 25] despite classifications on new images. One example applied in welding process is [62, 63]. It utilizes autoencoder deep learning technique to extract features of images process in laser welding. Another example of recent application of deep learning technique is [72]. It presents a method based on deep learning aims to extract information from photographs on spot welding. This monitoring system on the spot welding productive line shown better performance than the previous images analysis.
Not focused on welding arc analysis, but with good results, the work [73] proposed an automatic detection for weld defects in X-ray images. A classification model on deep neural network was developed. The accuracy rate of the proposed model was 91.84%. This was one more example of the potential of these techniques in welding area on images processing.
Today’s manufacturing environments has a rapid advancement on demand for quality products. Many techniques and methods are applied to correlate between process parameters and bead geometry. One of them is response surface methodology (RSM). It was applied by Sen in 2015 [74]. He made to evaluate the correlations between process parameters and weld bead geometry in double-pulsed gas metal arc welding (DP-GMAW). Santhana Babu [75] with the same technique got good results for predicting and controlling the weld bead quality in GTAW process. The problem of this method is that the researcher can find the equation, called response surface, by test and error. This can be very difficult. Many theoretical models have been defined to determine the process that occurs in the welding arc, including [76]. The main problems of these models were that they lose precision because it was very difficult to obtain a formula that contains all the complexity of these processes, as well as affirmed by Hang Dong in [77]. Mathematical models, based on machine learning techniques, have better results in problems as complex as this one. In the same paper, Hang Dong expressed the potential of these models.
One of the well-known and utilized regression algorithms is the least squares method. It was utilized in [78] to predict the seam position under strong arc light influence. Other work is [79] a LR model that is utilized to analyze the pool image centroid deviation and weld based on visual weld deviation measurement in GTAW process. The other technique is Gaussian process (GP) regression (GP), which was utilized in [77] to predict better performance in arc welding process of GTAW process.
An interesting method, utilized in [80], was Mahalanobis Distance Measurement (MDM). It was employed to determine welding faults occurrences. The same method was utilized in 2017 by Khairul Muzaka [81] on GMAW process to optimize welding current on a vertical-position welding. One problem of this method is that only correlate in function one input.
Bai and Lubecki [82] proposed a Localized Minimum and Maximum (LMM) analysis method in real time for welding monitoring system. The problem of LMM is that it exposes a simple function to measure the quality than not defining the complexity of the system. That is why, this work is limited only to the short-circuit transfer mode.
In 2017 by Junheung Park [83], a SVM was proposed with bootstrap aggregating that reduced the noisy on RSW data with computational efficiency. In this framework, other techniques as Generalized Regressive Neural Networks (GRNN) and Genetic algorithms for optimization were joined. This article demonstrates an increase in more complex computer science techniques for better analysis of welding processes. But the only way to know if all this was necessary is comparing with other techniques.
Some researchers already had this reference of advantages of these algorithms. Bo Chen in 2009 [84] utilized ANN to training the experimental obtaining data. The good result of ANN prediction was validated by D-S evidence theory information fusion. They have also been utilized for different purposes and in different welding processes such as in SAW process [85] and GMAW cold metal transfer (CMT) process [86], for predicting weld bead geometry; in GTAW process, for predicting the angular distortion considering the bead geometry [87]; in girth welded pipes process, for predicting residual stresses [88]; and in underwater wet welding process, for predicting the weld seams, geometric parameters [89].
For better results, ANNs have been mixed with other techniques. One example is [90], where ANN and Support Vector Machine (SVM) are utilized for welded defect detecting and monitoring on a laser welding process. The other technique is by Bo Chen and Shanben Chen [91] for predicting the penetration in GTAW process. But they used different ANNs to process information from different sensors, and finally, they used the predictive fuzzy integral method.
Another example is [92], for predicting bead height and width in GMAW process using ANN Fuzzy ARTMAP, like monitoring task.
The increase in computational resources has allowed an increase in the complexity of ANN architectures. These are called Deep Neural Networks (DNN). They, bit by bit, begin to be applied in the welding process. One of them utilized was in [93]. The model is based on a DNN architecture to make a study of the estimation of weld bead parameters. This article mixed data from different welding processes. This is a risk for results analysis since different processes can have different outcomes with the same input parameters.
Rao et al. [94] utilized Generalized Regressive Neural Networks (GRNN) technique for estimating and optimizing the vibratory assisted welding parameters to produce quality welded joints. But in this case, it does not have comparison with other algorithms.
Di Wu, in 2017 [95], wrote a paper that addresses to perform Variable Polarity Plasma Arc Welding (VPPAW) process. Deep Belief Network (DBN), DNN variant, and t-Stochastic Neighbor Embedding (t-SNE) were studied for monitoring and identifying the penetration values. Experimental comparisons and verifications expose better performance for DBN, 97.62% exactly. This reaffirms the good results offered by the learning models developed with these algorithms. This work does not take the advantage of DNN algorithms to analyze both images and sound in real time.
Figure 6 shows a summary of articles analyzed. It shows that ANNs are one of the most used techniques, but they do not always offer the best result. This demonstrates the need to make comparisons between various modeling techniques in order to define the best result, in terms of efficiency and computational cost.
Comparison between ANNs and ANN variations.
As it has been expressed in the previous sections, there are new techniques to analyze very complex systems. But they require expensive computational resources for their construction and sometimes for their execution. A comparison between models will allow to know which model has better results and which model can be the most effective to be utilized. This effectivity is measured in function of problem necessity, like the one shown in data mining (DM) methodologies and processes [16, 17].
An interesting comparison is Support Vector Machine (SVM) and ANN model, to identify weld groove state and weld deviation extraction in rotating arc narrow gap MAG welding (RANGMW) [96]. It presented SVM models with better results than ANN model.
One comparison with focus on time optimized was [97]. It utilized an ANN and ANN with differential evolutionary algorithm (DEA) separately. The results obtained by ANN using DEA were closer to ANN, but the computational time of ANN using DEA was shorter.
In the article [98], Response Surface Methodology (RSM) was compared with linear isotonic regression, regression (LR), regression trees, ANN, GP, and SVM, to evaluate mechanical properties in GMAW process. The results present that the DM models have poorer generalization on this research, because DM techniques require, to obtain acceptable results, a large amount dataset.
Sumesh in 2015 [99] compared Decision Trees (DT), ANN, Fuzzy Logic, SVM, and Random forest technique Weld Quality Monitoring in SMAW. The most efficient technique was Random forest. This shows that not always the most complex techniques offer the best results.
One of the few comparative analysis algorithms is Kumar’s paper in 2016 [100]. This paper explores Self-Organizing Maps (SOM) using as a mechanism for performing unsupervised learning, for comparing performance characteristics of various welding parameters which include welding power supplies and welders. Results obtained using SOM has been compared with the Probability Density Distributions (PDDs) obtained during statistical analysis. Voltage and current data analyzed using the SOM technique can also be utilized to evaluate the arc welding process. These studies demonstrate that there are other potential algorithms for welding process analysis. For that reason, it is necessary to evaluate and compare several of them to be agreed upon in a real-time process.
Other comparison in 2016 by Di Wu is [54]. The article compared a prediction model for Plasma Arc Welding based on Extreme Learning Machine (ELM) with ANN and SVM techniques. The ELM model had better generalization performance and was faster than others. This potentiality was established too by Nandhitha in 2016 [106]. He utilized GRNN and Radial Basis Networks (RBN) for torch current prediction in GTAW process. The torch current deviation was 98.95 % accuracy for the best result of GRNN.
In 2016 too, Kyoung-Yun Kim [107] discusses that in Resistance Spot Welding (RSW) process. He examined the prediction performance with GRNN and k-Nearest Neighbor (kNN) algorithms. The results indicate that with smaller k of kNN, the prediction performance measured by mean acceptable error has increased.
Other quality welding article was Xiaodong Wan in 2017 [102]. It proposed a Probabilistic Neural Network (PNN) model for quality prediction in large scale RSW process. In this case, the PNN model was more appropriate in quality level classification than the Back Propagation Neural Network.
The one of the last articles with direct DM techniques and welding relation is of Yiming Huang in 2017 [103]. This is an investigation of porosity on pulsed gas tungsten arc welding (P-GTAW) with an X-ray image analysis. To detect, an Empirical Mode Decomposition (EMD) and Spectral Analyses were made based on DM.
In 2017, Petković [104] predicted the laser welding quality by training data for the computational intelligence methodologies and support vector regression (SVR). SVR is a novel variant of SVM for regression task. This article made a comparison between SVR, ANN, and GP. It is another example that in certain problems, less complex algorithms can offer better results.
Table 2 presents a series of articles that were based on the monitoring and quality of the welding processes. The column
Author | Year | Welding process | Sensors | Data preparations | Modeling | Online | Compare |
---|---|---|---|---|---|---|---|
Saini [52] | 1998 | GMAW | Sound | Classic | No | Yes | No |
Yue [69] | 2009 | Pipeline welding | Visual | Classic | Theoretical model | No | No |
Chen [64] | 2010 | LBW/GMAW | Visual | Classic | Yes | No | |
Horvat [53] | 2011 | GMAW | Sound | Classic | No | Yes | No |
Gao [78] | 2011 | GTAW | Visual | Classic | LR-ANN | No | No |
Feng [80] | 2012 | GMAW | Standard | Classic | MDM | Yes | No |
Fidali [45] | 2013 | GMAW | Infrared | Classic | Statistical analysis | Yes | No |
Sreedhar [48] | 2013 | GTAW | Infrared | Classic | Statistical analysis | Yes | No |
Kalaichelvi [101] | 2013 | GMAW | Standard | Classic | GA-Fuzzy | Yes | No |
Kumar [97] | 2014 | GMAW | Visual | Classic | ANN, ANN-DEA | Yes | Yes |
Deyong You [90] | 2015 | Laser welding | Photodiode, spectrometer | WPD-PCA | FFANN-SVM | Yes | No |
Sumesh [99] | 2015 | SMAW | Sound | Classic | Some DM (RF) | Yes | Yes |
Kumar [100] | 2016 | SMAW | Standard | Classic | PDDs, SOM | No | Yes |
Muzaka [81] | 2016 | GMAW | Standard | Classic | MDM | Yes | No |
Bai [82] | 2016 | GMAW | Standard | Classic | LMM | Yes | No |
Park [83] | 2017 | RSW | Standard | Classic | GRNN-SVM | Yes | No |
Wan [102] | 2017 | LSRSW | Standard | Classic | ANN (BP), ANN (Prob) | Yes | Yes |
Huang [103] | 2017 | P-GTAW | Visual | Classic | DM, EMD | No | Yes |
Petković [104] | 2017 | Laser welding | Multiples | Classic | SVM, ANN, GP | Yes | Yes |
Muniategui [72] | 2017 | RSW | visual | DL, classic | Fuzzy | Yes | Yes |
Wan [105] | 2017 | GTAW | visual | Classic | ANN and fuzzy | Yes | No |
Table articles with quality objective.
Author | Year | Welding process | Sensors | Data preparations | Modeling | Online | Compare |
---|---|---|---|---|---|---|---|
Bo Chen [84] | 2009 | GTAW | Multiples | Classic | ANN-DS | No | No |
Bo Chen [91] | 2010 | GTAW | Multiples | Classic | ANN-fuzzy | No | No |
Seyyedian [108] | 2012 | GTAW | Standard | Classic | ANN | Yes | No |
Li [79] | 2014 | GTAW | Visual | Classic | LR | No | No |
Bo Chen [89] | 2014 | UWW | Visual | Classic | ANN | Yes | No |
Li [96] | 2014 | RANGMW | Visual | Classic | SVM, ANN | Yes | Yes |
Escribano-García [98] | 2014 | GMAW | Standard | Classic | RSM, some DM | Yes | Yes |
Sen [74] | 2015 | DP-GMAW | Standard | Classic | Taguchi-RSM | No | No |
Keshmiri [93] | 2015 | SAW, GMAW, GTAW | Standard | Classic | DNN | Yes | No |
Wu [54] | 2016 | VPPAW | Sound | Classic | ELM, ANN, SVM | Yes | Yes |
Lv [55] | 2016 | GTAW | Sound | Classic | BP-Adaboost | Yes | Yes |
Dong [77] | 2016 | GTAW | Standard | Classic | GPR | Yes | No |
Sarkar [85] | 2016 | SAW | Standard | Classic | MRA and ANN | Yes | Yes |
Rong [87] | 2016 | GTAW | Standard | Classic | ANN | Yes | No |
Rios-Cabrera [92] | 2016 | GMAW | Visual | Classic | ANN fuzzy ARTMAP | Yes | No |
Nandhitha [106] | 2016 | GTAW | Thermography | Classic | ELM, RBN, GRNN | Yes | Yes |
Kim [107] | 2016 | RSW | Standard | Classic | kNN, GRNN | Yes | Yes |
Aviles-Viñas [109, 110] | 2016 | GMAW | Visual | Classic | ANN-fuzzy | Yes | No |
Pavan Kumar [86] | 2017 | GMAW CMT | Standard | Classic | ANN | Yes | No |
Mathew [88] | 2017 | Girth welds | Standard | Classic | ANN | Yes | No |
Di Wu [95] | 2017 | VP-PAW | Visual, sound | Classic | t-SNE and DBN | No | No |
Table articles with prediction objective.
Author | Year | Welding process | Sensors | Data preparations | Modeling | Online | Compare |
---|---|---|---|---|---|---|---|
Chen [66] | 2000 | P-GTAW | Double-visual | Classic | ANN-learning control | Yes | Yes |
Chen [111] | 2009 | GTAW | Visual | Classic | ANN-fuzzy | Yes | No |
Malviya [112] | 2011 | GMAW | Standard | Classic | ANN-PSO | Yes | No |
Hailin [105] | 2012 | GMAW | Visual | Classic | ANN and fuzzy | Yes | No |
Cruz [113] | 2015 | GMAW | Visual | Classic | ANN and fuzzy | Yes | No |
Günther [63] | 2016 | Laser welding | Visual | DL | DL-RL | Yes | No |
Santhana [75] | 2016 | GTAW | Standard | Classic | RSM | Yes | No |
Sharma [114] | 2016 | SAW | Standard | Classic | RSM and fuzzy | Yes | No |
Moghaddam [115] | 2016 | GMAW | Visual | Classic | ANN-PSO | Yes | No |
Lv [56] | 2017 | GTAW | Sound | Classic | ANN | Yes | No |
Rao [94] | 2017 | Vibratory Welding | Standard | Classic | GRNN | Yes | No |
Pengfei Hu [116] | 2017 | GMAW | Standard | Classic | Math-model—fuzzy | Yes | No |
Table articles with control objective.
Defining which of the techniques is more effective for our problem also helps in the effectiveness of a future process of intelligent control.
The intelligent control approach offers interesting perspectives since it is able to provide methodologies that allow to perform automatically some of the tasks typically performed by humans [117]. This combines with data mining models.
One intelligent control tendency utilized is a fuzzy method with ANN model. Example of this was [111] on GTAW process for predicting the dynamic of the weld pool; and in [105] for GMAW pipe-line welding, to improve the welding quality.
Another example was [113], on GMAW process, for modeling and control of weld bead width. Other example of fuzzy methods but different model techniques was [114]. It was applied for better control purpose of bead geometry parameters in submerged arc welding (SAW) process. This article proposed the response of a fuzzy logic approach with surface methodology (RSM). Demonstrating that any model obtained from a welding process can be integrated into a control system. As long as it meets time demands.
Conventional and intelligent control methods were investigated by [67] in P-GTAW process. This work made a comparison with PID control, fuzzy control, and neuron self-learning PSD control. It had better performance. This article highlights the advantage of learning-based control.
Other optimization based in learning was [115]. It proposed ANN model with a Particle Swarm Optimization (PSO) algorithm to optimize weld bead geometry characteristics on the GMAW process. The ANN-PSO model obtained an efficient optimization and multi-criteria modeling.
An emerging learning-based control system was used by Günther in [62, 63] for laser welding control. This technique is called reinforcement learning (RL). It is a machine learning branch. It is focused on decision-making by learning process [118]. Control learning can be an optimization-based method like Q-learning algorithm. It can be used to solve optimal control problems like expressed in [119].
Günther’s study [63] is one of the few RL studies for laser welding system. This makes this work an important contribution to welding process engineering. RL is a new technique open now in welding process with noble success in other areas like appearing in [120, 121, 122, 123].
These techniques of data analysis based on learning, as appearing in this article, is not yet widespread in welding process area. A bibliometric analysis among the authors studied in this research, presents a very little relationship between them. Figure 7 exposes this. The small dimensions of the authors’ clouds (articles with welding process and new data analysis techniques) and their relationships (joint publications) show little maturity in the interrelation of these areas.
Bibliometric analysis: authors’ interrelationship.
Some of the works demonstrate a small approximation between the areas, fulfilling the interdisciplinarity that Industry 4.0 advocates. Achieving this interdisciplinarity implies new study processes, defining new methodologies that unify the potential of these two areas. The needs of the modern world are going to make this happen in a short time. The new data analysis conception in welding processes area will be an acceleration in obtaining new and better models, more efficient predictions, and controls.
Several articles about the welding process were analyzed. These allowed to determine for each data mining stage how it is possible to optimize the results to obtain a good result of process analysis. Several analysis algorithms of the welding process were shown, and it was demonstrated that the comparison between them can make the process analysis more efficient and less expensive. The potential of learning-based techniques was described, because computational resources are becoming cheaper, and more quality information of welding process can be obtained. All these premises aligned with the so-called Industry 4.0, where a set of technologies that allow a fusion of physical and digital world, create a more intelligent and dynamic system.
The authors would like to acknowledge IntechOpen, Brasilia University, CNPq, CAPES, and PPMEC-UnB, and also to professors Alysson Martin Silva, and Guillermo Albarez Bestard.
Nanotechnology allows the development of nanomaterials with controllable physical, chemical, and biological properties [1, 2, 3, 4]. These properties are controlled according to the nanomaterials’ size and shape, enabling the development of new innovative technologies, from new device development to tools in the health field [4, 5, 6, 7, 8, 9]. In this context, nanobiotechnology is a recent field emerging from science, which establishes an interface between biology and nanotechnology, evaluating and assigning new functionalized nano biosystems [8]. Thus, this multidisciplinary research field has great potential in developing improved medical engineering [1, 10].
Nanoparticles can be amorphous or crystalline (nanocrystals), and this difference reflects directly on the physical, chemical, and biological properties. Spanó et al. demonstrated that zinc oxide (ZnO) nanocrystals are more biocompatible when compared to amorphous nanoparticles [11]. Amorphous nanoparticles have a long-range disorder of their atoms and are more reactive [12]. On the other hand, nanocrystals show the periodicity of atoms forming crystalline arrangements and consequently fewer defects and less reactivity [13, 14, 15].
Nanocrystals (NCs) are often and successfully applied in several sensors, such as colorimetric, fluorescence, surface plasmon resonance, and electrochemical [16]. Regarding electroanalytical chemistry, conductive nanostructured crystals are interesting for application in electrochemical sensing due to their well-known ability to improve the catalytic activity, the electron transfer speed, and the conductivity of the sensors. Furthermore, the deposition of nanocrystals over electronic surfaces can increase the superficial area and amplify the analytical signal, enhancing the sensitivity regarding the detection of target analytes [17]. Nowadays, nanocrystal-based sensors have been widely explored in various applications and attracted the attention of several researchers [18].
Nanoparticle drug delivery can be used to target the tissue, promote the slow-release, protect against degradation, and diminish toxicity [19, 20]. The pharmacokinetic properties of a compound of biological activity are, among other factors, related to its solubility. The low solubility will result in problems from absorption until elimination. So, it is essential to search for alternatives, and they can increase the solubility of drugs without interfering in their pharmacological activity. Several active compounds are usually poorly soluble in aqueous media [21, 22]. A vast literature reports the possibility of complexation between lipophilic organic molecules appropriately sized, inorganic ions, and other species, with cyclodextrin, dendrimers, and liposomes. Whether for the drug’s application for pharmaceutical use, the nanocrystal compounds are essential on the medical and economic side [23, 24].
The development of biomaterials arouses tremendous scientific and clinical interest, given the possibility of replacing, in part or whole, human bones and/or favoring bone regeneration, both in the craniofacial complex and in other parts of the skeleton. Therefore, it is expected that the materials have osteoconductive properties (materials that function as a support surface for adhesion and proliferation of osteoblastic cells, which promote the formation of mineralized bone tissue), osteoinduction (materials that contain inductive proteins present in the matrix bone tissue and are capable of inducing differentiation of undifferentiated mesenchymal cells into chondroblasts or osteoblasts), osteogenesis (materials that have viable osteoblasts, capable of determining the formation of the new bone when grafted into the host tissue) and/or osteopromotion (materials that constitute physical barriers for the anatomical isolation of the site under repair, aiming at the selection of cells that promote the restoration, while excluding competing for inhibitory cells) [25]. This chapter book will comment specifically on titanium dioxide nanocrystals in dental and orthopedic applications.
Therefore, this chapter shows the innovative results obtained by the group of carbon-based, semiconductor, and magnetic nanocrystals that can be used in biosensors and biomedical applications, further strengthening the development of new tools.
This section shows nanocrystals’ results in improved electrochemical sensors and their use as theranostic tools in biomedical applications. We will demonstrate how graphene nanostructures and CdSe/CdS MSQDs can improve sensors sensitivity. In the biomedical applications, we will show CdSe/CdS MSQDs and cobalt ferrite (CoFe2O4) NCs to drug deliveries and biocompatible titanium dioxide (TiO2) NCs in osseointegration processes and their bio-location.
Graphene has been the nanostructured material most utilized in electroanalytical applications due to its unique features [26]. Graphene consists of a single layer of carbon atoms in the sp2 hybridization organized in a honeycomb structure with six-membered rings, yielding 2D nanocrystals [27]. Some advantages of using graphene in electrochemical sensors are enhanced conductivity, decreased overpotentials, increased electroactive areas, and enhanced charge transfer rate [28]. Graphene is usually synthesized by the chemical reduction of graphene oxide [29, 30].
In this field, the advantages of using graphene-based nanocrystals in electroanalytical sensing have been previously demonstrated by several researchers and our research group [31]. We have reported the modification of a glassy carbon rod electrode (GCRE) with reduced graphene oxide doped with copper nanoparticles (RGO-CuNP). The synthesis of the RGO used in this work was carried out by the modified Hummers’ method [32]. This GCRE modified with RGO-CuNP was coupled, for the first time, to a paper-based electrochemical platform and applied in the electroanalysis of analytes of clinical interest, as illustrated in Figure 1.
Representation of the modification of the GCRE with RGO-CuNP, coupling of the modified sensor to the paper-based electrochemical platform such as used in work, and voltammetric responses of unmodified and modified electrodes in the simultaneous electroanalysis of paracetamol and caffeine.
The modified sensor was thoroughly studied, optimized, and characterized. The results showed that the modification with RGO-CuNP significantly improved the electrochemical properties of the working electrode. In comparison with the unmodified GCRE, the RGO-CuNP sensor presented lower peak potentials, better sensitivity (ca. two-fold higher), lower electron transfer resistance (857 vs. 21,497 Ω), and larger electroactive area (0.067 vs. 0.040 cm2). These improvements are directly related to the modification with RGO-Cu nanocrystals, which are responsible for enhancing the conductivity and increasing the superficial area of the GCRE. As proof of concept, this modified sensor was employed to determine paracetamol and caffeine in real urine samples simultaneously. These two analytes were successfully quantified in low levels without matrix interferences, and the results showed excellent concordance with high-performance liquid chromatography used to validate the method. So, it evidences some of the advantages of using graphene-based nanocrystal in sensing platforms.
In this context, quantum dots (QDs) are among the most explored nanomaterials nowadays. The synthesis and practical application of QDs are among the main focuses in the development directions of nanotechnology [33]. QDs are semiconductor nanocrystals with optical and electrical properties that are widely employed in sensing applications. Another exciting feature of QDs is that properties such as size, shape, composition, and structure can be controlled and tuned. It allows obtaining QDs with unique properties according to the desired application [34, 35].
Although, promising, conventional QDs have some drawbacks that include moderate stability, limited luminescence spectra, and large size to some applications. At this point, the magic-sized quantum dots (MSQDs) are a class of nanocrystals that show smaller particle sizes, broader spectra, and more excellent stability than conventional QDs [36]. Among other applications, the MSQDs are a promising nanomaterial for electrochemical sensing use. The small size (in nanometric scale) and the electrical properties of these nanocrystals can significantly increase the surface area and the conductivity of the sensors.
Our research group recently explored, for the first time, the application of MSQDs for the modification of electrochemical sensors [37]. This pioneering work proposed a simple and inexpensive paper electrochemical device (PED) whose carbon-based working electrode was modified with CdSe/CdS MSQDs. The three-electrode setup (working, counter, and pseudo-reference) was fabricated on the paper substrate by a simple pencil-drawing method. At the same time, the CdSe/CdS MSQDs were synthesized according to the method described by Silva et al. [36, 38, 39]. This PED was modified with CdSe/CdS MSQDs to demonstrate the analytical feasibility and applied for clinical quantification of dopamine in biological samples, as represented in Figure 2.
Picture of the paper electrochemical device containing the pencil-drawn carbon electrodes used in work, representing a modification of the working electrode with CdSe/CdS MSQDs, and the voltammetric dopamine response regarding the unmodified and modified electrodes.
Electrochemical and morphological techniques investigated the miniaturized CdSe/CdS MSQDs-based PED. This modified PED presented improved analytical signal (ca. 46% higher), lower charge transfer resistance (32 vs. 169 Ω), and larger superficial area (0.28 vs. 0.14 cm2) in comparison with the unmodified PED. It can be attributed to CdSe/CdS nanocrystals in the sensor, which was also confirmed by microscopy analysis. The electroanalysis of dopamine in real human blood serum samples was successfully carried out, and the limit of detection obtained was lower than other recent reports that utilize more complex electrochemical platforms for detecting the same analyte. In this way, MSQDs have been shown as a promising nanomaterial to be explored in electrochemical sensing.
In the last decade, several nanostructured systems for the delivery of chemotherapeutic agents have been developed to eliminate tumor cells. However, most of these systems cannot reach specific tumor cells without adequate control of these drug release processes, resulting in serious side effects [40, 41]. It is necessary to direct efforts to improve ideal drug distribution systems to release stimuli and selectively target cancer cells. Thus, quantum dots, liposomes, magnetic nanoparticles, and TiO2 nanocrystals have enormous potential.
Quantum dots have been the subject of extensive investigations in different science and technology areas in the past years [42, 43]. There are few studies of MSQDs, even though they exhibit features such as tiny size, higher fluorescence quantum efficiency, molar absorptivity greater than traditional QDs, and highly stable luminescence in theranostic, which refers to the simultaneous integration of diagnosis and therapy [36, 39, 44, 45].
Our group investigated the first study about the core-shell MSQDs by analyzing the electrochemical behavior of CdSe/CdS MSQDs immobilized on a gold electrode modified with a self-assembled cyclodextrin monolayer using cyclic voltammetry and electrochemical impedance spectroscopy techniques [46]. The work showed a good interaction between the thiol group from thiolated cyclodextrin and CdSe/CdS MSQDs (Figure 3a). The proposed method was successfully applied to encapsulation studies of Mangiferin, a natural antioxidant compound, and cyclodextrin associated with the CdSe/CdS MSQDs, and the response was compared with that of the modified electrode without MSQDs. The fluorescence study revealed that CdSe/CdS MSQDs emit blue light when excited by an optical source of the wavelength of 350 nm, and a significant increase in fluorescence and absorbance intensity is observed from the core-shell CdSe/CdS MSQDs when quantities of Mangiferin are added to the solution containing thiolated cyclodextrin. CdSe/CdS MSQDs are optically and electrochemically sensitive and can be used to detect and interact with compounds encapsulated in cyclodextrin and can be applied in theranostic.
(a) CdSe/CdS MSQDs immobilized on a gold electrode modified with a self-assembled cyclodextrin to encapsulation studies of Mangiferin, and (b) illustration of liposome with MSQDs (top panel) when MSQDs are (i) inside or (ii) outside and optical image the scale bar is 1 mm (bottom panel).
Because of their reduced size, lipophilic nanoparticles of less than 100 nm can cross the brain-blood barrier by diffusion, allowing the drug delivery directly to the Central Nervous System (CNS) [47]. Neurodegenerative diseases (ND) such as Alzheimer’s, Parkinson’s, strokes, glioblastoma, Huntington’s, amyotrophic lateral sclerosis may be treated differently with this approach [19]. Just like liposomes, polymeric nanoparticles may be environmentally sensitive to drug release, such as temperature change, pH change, among others. These systems may be combined therapy, delivering two or more drugs, allowing different therapy combinations.
The group has also been developing liposomes containing CdSe/CdS MSQDs aiming at a new luminescent tool for drug delivery. Figure 3b shows the illustration of liposomes with MSQDs (top panel); when MSQDs are (i) inside or (ii) outside and optical image, the scale bar is 1 mm (bottom panel). Therefore, we demonstrate that CdSe/CdS MSQDs can be used in drug delivery systems, which serve as photostable fluorescent reporters. A combination of MSQDs with liposomes is a powerful theranostic tool since it is possible to monitor their location via luminescence in addition to drug delivery.
Since the 1990’s Liposomal Amphotericin B has been available in the market, being one of the oldest and most clinical used nanoparticle formulations in the treatment of leishmaniasis. In 1978, Alvin et al. proved that the use of liposomal leishmanicidal drugs could enhance 700 times the efficacy [48]. Liposome functionalization is another advance that can enhance circulation time and release drugs according to temperature change and pH change; magnetic prepared liposomes can be target-directed by applying a magnetic field, and ligands in the lipidic bilayer can actively target cellular types [49]. Thus, the group has been working to develop drug systems containing liposomes and nanocrystals.
The study of bioactive substances by electrochemical and UV-visible spectroscopic methods is already very conceptual. The association of magnetic nanoparticles has emerged as a new bias of these techniques. We group reported the interaction between the molecule LQM10, a derivative of guanylhydrazone, with the CoFe2O4 NCs coated with polyamidoamine dendrimer (PAMAM), generating a nanocarrier to benefit LQM10 (Figure 4). The PAMAM dendrimer has empty spaces that change according to its generation. In these places, as well as cyclodextrins, “guest-host” interactions can occur, where the hydrophobic molecule can interact with dendrimers by hydrogen bonds, ionic bonding, or hydrophobic interactions, being possible interaction with LQM10 due to the tert-butyl group attached to its ring, which gives it a hydrophobic character, as well as with CoFe2O4 NCs. In addition to this type of interaction. PAMAM can make covalent and non-covalent bonds through its primary and tertiary amine groups, which would also be possible by observing the structure of LQM10. Both interactions can occur in an isolated or simultaneous way, making it possible for a single molecule of PAMAM to interact with several other substances, which enables its association with CoFe2O4 NCs, producing a better nanocarrier for LQM10 [50].
Illustrative scheme of the nanocarrier composed by the PAMAN molecule, the black spheres represent the CoFe2O4 NCs and the LQM10 molecule.
The magnetic properties of CoFe2O4 NCs and each nanocarrier were confirmed by a vibrating sample magnetometer and field-effect calorimetry. LQM10 showed good interaction corroborating with the results of UV-visible and electrochemistry data. The heat generation by magnetic hyperthermia of CoFe2O4 NCs in the presence of PAMAM G3 and LQM10 was observed, demonstrating the association of promising nanocarriers (PAMAM G3 and CoFe2O4 NCs) with anticancer substances and their applicability as magnetic hyperthermia [50].
The implantation of material inside biological tissues must meet a minimum requirement, called biocompatibility, defined as a biomaterial’s ability to perform the desired therapeutic function without triggering any undesirable local or systemic effect, generating the most cellular or tissue response. Therefore, optimizing clinical therapeutic performance should be as beneficial as possible [51]. After application, an interaction occurs between the host’s immune system and the implanted biomaterial, leading to a specific cellular reaction to the biomaterial [52]. Proteins play a crucial role in the interaction between biomaterials and cells or tissues. Thus, the absorption of proteins on the material surface is the first event of this interaction, which is decisive for the subsequent cell growth processes, differentiation, and extracellular matrix formation [53].
The deliberate, accidental implantation of any foreign material into living tissues causes a response, and it is not the response itself but the extent, intensity, and duration that define biocompatibility. The ideal response of biological tissues to a biomaterial is when the initial inflammatory response resulting from the surgical procedure is quickly resolved, without the presence of a chronic inflammatory infiltrate or the development of an immune response. Thus, the biomaterial must be biocompatible and have characteristics that include predictability, clinical applicability, absence of transoperative risks and minimal postoperative sequelae, and acceptance by the patient. It is also expected that this biomaterial is not carcinogenic, that it presents adequate chemical and biological stability, mechanical and elastic resistance, and has low cost [54].
The biomaterial is a natural or synthetic material intended to interact with biological systems to assess, treat, augment, or replace an organism’s organ, tissue, or function [51]. The primary function of biomaterials is to replace damaged tissue and passively assume its function, selection, and manufacture, based on the imitation of the chemical and physical properties of natural tissue, causing minimal response as a foreign body [53].
Since the early 1970s, various synthetic bone substitutes have been developed to minimize the difficulties inherent in using autogenous bone grafts and homogeneous and heterogeneous bone implants [25]. The main advantages of grafts created from synthetic materials through bioengineering are biocompatibility and good reabsorption [55]. The alloplastic materials most commonly used in the medical-dental field are metals or metal alloys, ceramics, polymers, composites, and bioactive glasses [25]. Despite the wide variety of organic and synthetic materials capable of replacing bone tissue or stimulating reparational osteogenesis, there is still no material that meets all the desired requirements.
Titanium dioxide (TiO2) is a semiconductor that absorbs and emits in the ultraviolet region with numerous applications in biomedical fields such as cosmetics, medicines, and pharmaceutical products [56, 57, 58]. This material has three crystalline phases: anatase, brookite, rutile, and physical and biological properties [59]. The anatase phase is more electroactive than the rutile phase, having greater genotoxicity and photocatalytic effects [60, 61, 62, 63]. The TiO2 NCs have shown great potential for use in implants due to their excellent physical, chemical, and biological properties, such as high specific surface area, ability to provoke positive cellular response and stability in body fluids, is suitable for the propagation, proliferation, and differentiation of osteoblast cells [56, 64]. Thus, will show exciting results obtained by the group using TiO2 nanocrystals as well as their luminescence bio-location.
The porous structure of TiO2 nanotubes increases bone regeneration and repair, presenting good osteointegration, being used as a graft and biological fixation element for implants [57, 65, 66, 67, 68, 69, 70]. In an experimental study carried out by our research group using TiO2 NCs, adequate osteointegration in bone failure in the calvary of rats was evidenced, with the presence of a large amount of newly formed tissue, suggesting effective osteoinductive action, as can be seen in Figure 5.
Histological section of rats’ calvaria: (a) fibrous connective tissue (asterisk); bone tissue (arrow); and (b) TiO2 NCs (arrows), bone tissue (arrow), and neoformed fibrous connective tissue (asterisk) (hematoxylin and eosin staining, 400×).
Despite the promising findings with TiO2 NCs, it is essential to report that there may be a high contamination rate and post-surgical infection since, currently, the spread of antibiotic-resistant bacteria is a worrying threat to human health.
In this context, it is essential to establish new antimicrobial strategies, in which the idea of coating device surfaces with active antimicrobial metals is considered one of the essential strategies. Therefore, bimetallic corrosion is inevitable, in which TiO2 photocatalytic nanomaterials, in the anatase form, offer more significant advantages for antimicrobial purposes [71]. Still, the photocatalytic activity of TiO2 under exposure to ultraviolet radiation results in disinfectant properties, mainly related to the generation of reactive oxygen species [72].
In this perspective, the sensitive and accurate detection of biological analytes in low concentrations is another application of TiO2 nanostructures that is beneficial for biomedical research and clinical diagnosis. There has been significant interest in applying TiO2 detection in biosensors [57, 67]. Therefore, those reported in the literature point out that TiO2 nanocrystals are inert and safe structures when exposed to the human organism, thus contributing to new promising nanotechnologies with the biomedical application.
Luminescence is related to some materials’ ability to light emissions. This excitation energy (absorbed energy) can be obtained from different sources: photons usually in the ultraviolet region of the electromagnetic spectrum (emission called photoluminescence), electrical energy (electroluminescence), electron beam (cathodoluminescence), physical impact (gives rise to triboluminescence) and heating the luminophore (results in thermoluminescence) [73, 74]. Photoluminescent materials are often called phosphors or luminophores [73]. Efficient luminophore requirements are efficient absorption of light in a suitable spectral region; chemical stability of the excited electronic state populated after light absorption; high conversion efficiency to the excited luminescent state; a long lifetime of excited state luminescence; high luminescent efficiency [75].
Photoluminescent materials require a host crystalline matrix, such as TiO2, as well as an activating ion, such as lanthanides. Lanthanide ions are known to have characteristic luminescence (high color purity). Among lanthanides, the europium ion (Eu3+) is one of the most used for biomarking due to its intrinsic electronic spectroscopic properties in the visible region under excitation in the ultraviolet region [76, 77].
Compounds with trivalent europium ions emit red light, with emission spectra of thin bands of approximately 614 nm. Therefore, it has been applied to investigate the properties and functions of biochemical systems and the determination of biologically active substances. In this context, we find reports of its application mainly as spectroscopic probes in the study of biomolecules [78]; in biological tracers to follow the path taken by medicines in the human organism and animals; as markers in immunology (fluoroimmunoassays) [79], as well as contrast agents in non-invasive diagnosis of pathologies in tissues by nuclear magnetic resonance imaging [80].
In a study carried out by our research group with TiO2 NCs doped with Eu3+, in the culture of mesenchymal stem cells, isolated from bone marrow cells, the presence of these nanocrystals was observed in the cytoplasm of the cells after 24 hours of incubation, not being found in the cell nucleus, suggesting the absence of cytotoxicity and genotoxicity (Figure 6).
Fluorescence microscopy of mesenchymal stem cells treated with europium doped TiO2 NCs: (a) culture medium with 50 μg of mesenchymal stem cells; and (b) culture medium with 100 μg of mesenchymal stem cells.
Therefore, this chapter showed nanocrystals inserted in biosensors and their use in drug delivery tools or biomaterials. Graphene and magic-sized quantum dots into biosensors enable an increase in sensitivity and specificity, making the development of nanotechnological platforms in biological diagnosis possible. In theranostic applications, magic-sized quantum dots, magnetic nanoparticles, and TiO2 nanocrystals can be innovative drug delivery tools and dental and orthopedic applications. Thus, the fabrication of nanomaterials with interesting properties makes it possible to generate several potential tools to improve electrochemical sensors and in theranostic applications.
This work was supported by CNPq, CAPES, FAPEAL, and FAPEMIG.
The authors declare no conflict of interest.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
\n\n\n\nWith the purpose of protecting our Authors' copyright and the transparent reuse of Open Access content, IntechOpen has developed an Attribution Policy for works published under Creative Commons licenses.
\n\n\n\nIntechOpen is committed to disseminating high-quality scientific research in a manner that exemplifies the best practice in scholarly publishing. IntechOpen is an official member of the Committee on Publication Ethics (COPE), which advocates the maintenance of the highest ethical standards for all parties involved in the act of publishing, including Authors, Academic Editors of the book, Peer Reviewers, the publisher and Societies, where applicable.
\n\nIn line with publication ethics practices recommended by COPE, ICMJE, and other similar organizations, IntechOpen's contributing Authors, Academic Editors, and Peer Reviewers are required to declare fully all possible conflicts of interest.
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\n\nAll scientific works are subject to Peer Review prior to publishing. IntechOpen is a member of the Committee on Publication Ethics (COPE) and all participating referees and Academic Editors are expected to review submitted scientific works in line with the COPE Ethical Guidelines for Peer Reviewers where applicable.
\n\n\n\nThe Internet has changed the dynamics of scholarly communication and publishing which is why we find it necessary to clearly indicate our stance on what we consider to be a published scientific work. A significant number of working papers, early drafts, and similar works in progress are shared openly online between members of the scientific community. It has become common practice for researchers to announce their work on a personal website or a blog in order to gather comments and suggestions from other researchers. Such works and online postings are ‘published’ in the sense that they are made publicly available, but this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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\n\nIntechOpen publishes books in the English language. If you are interested in the translation of Book Chapters, please check IntechOpen's Translation Policy.
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\n\nOnline First Chapters are considered published on the day they are posted and are citable from that date.
\n\nChapters will remain listed as Online First until the final versions of the books are published online. Following publication of the full monograph, Chapters will be redirected from the Online First version and will be available only through the final link of the official published page.
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His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. 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His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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