jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Quan Zhou | Wenqian Huang | Xi Tian | Yi Yang | Dong Liang
Source: Journal of the Science of Food and Agriculture (article, 2021-01-16)
Field: Chemistry — Analytical Chemistry

Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting Abstract BACKGROUND Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed. RESULTS First, visible and near‐infrared (NIR‐visible) hyperspectral images were obtained. Savitzky–Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non‐embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non‐embryoid forms was 97.78% and 98.15%, respectively. CONCLUSION The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry Hyperspectral imaging ; Convolutional neural network ; Identification (biology) ; Pattern recognition (psychology) ; Pixel ; Artificial intelligence ; Sample (material) ; Voting ; Smoothing ; Mathematics ; Computer science ; Botany ; Biology ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Sunil Kumar Sharma | Amit Kumar | Gaurav Sharma | Dai‐Viet N. Vo | Alberto García‐Peñas | Omid Moradi | Mika Sillanpää
Source: Chemosphere (review, 2021-11-20)
Field: Materials Science — Materials Chemistry

RETRACTED: MXenes based nano-heterojunctions and composites for advanced photocatalytic environmental detoxification and energy conversion: A review MXenes ; Photocatalysis ; Materials science ; Nanotechnology ; Heterojunction ; Hydrogen production ; Catalysis ; Chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Norsuhaili Kamairudin | Seng Soi Hoong | Luqman Chuah Abdullah | Hidayah Ariffin | Dayang Radiah Awang Biak
Source: Molecules (article, 2021-01-27)
Field: Materials Science — Polymers and Plastics

Optimisation of Epoxide Ring-Opening Reaction for the Synthesis of Bio-Polyol from Palm Oil Derivative Using Response Surface Methodology The development of bio-polyol from vegetable oil and its derivatives is gaining much interest from polyurethane industries and academia. In view of this, the availability of methyl oleate derived from palm oil, which is aimed at biodiesel production, provides an excellent feedstock to produce bio-polyol for polyurethane applications. In this recent study, response surface methodology (RSM) with a combination of central composite rotatable design (CCRD) was used to optimise the reaction parameters in order to obtain a maximised hydroxyl value (OHV). Three reaction parameters were selected, namely the mole ratio of epoxidised methyl oleate (EMO) to glycerol (1:5-1:10), the amount of catalyst loading (0.15-0.55%) and reaction temperature (90-150 °C) on a response variable as the hydroxyl value (OHV). The analysis of variance (ANOVA) indicated that the quadratic model was significant at 98% confidence level with (<i>p</i>-value > 0.0001) with an insignificant lack of fit and the regression coefficient (R<sup>2</sup>) was 0.9897. The optimum reaction conditions established by the predicted model were: 1:10 mole ratio of EMO to glycerol, 0.18% of catalyst and 120 °C reaction temperature, giving a hydroxyl value (OHV) of 306.190 mg KOH/g for the experimental value and 301.248 mg KOH/g for the predicted value. This result proves that the RSM model is capable of forecasting the relevant response. FTIR analysis was employed to monitor the changes of functional group for each synthesis and the confirmation of this finding was analysed by NMR analysis. The viscosity and average molecular weight (MW) were 513.48 mPa and 491 Da, respectively. Response surface methodology ; Polyol ; Hydroxyl value ; Coefficient of determination ; Glycidol ; Central composite design ; Glycerol ; Catalysis ; Palm kernel oil ; Polyurethane ; Raw material ; Chemistry ; Viscosity ; Organic chemistry ; Materials science ; Chemical engineering ; Palm oil ; Mathematics ; Chromatography ; Composite material ; Food science Polymer composites and self-healing ; Biodiesel Production and Applications ; biodegradable polymer synthesis and properties
Authors: João P. Vareda | Carlos A. García‐González | Artur J. M. Valente | Rosana Simón‐Vázquez | Marina Stipetić | Luísa Durães
Source: Environmental Science Nano (article, 2021-01-01)
Field: Chemistry — Spectroscopy

Insights on toxicity, safe handling and disposal of silica aerogels and amorphous nanoparticles The toxicity and ecotoxicity effects, handling and disposal of synthetic amorphous silica nanoparticles and aerogels are reviewed and discussed. Ecotoxicity ; Amorphous silica ; Amorphous solid ; Nanoparticle ; Aerogel ; Toxicity ; Materials science ; Environmental safety ; Nanotoxicology ; Chemical engineering ; Environmental chemistry ; Waste management ; Nanotechnology ; Chemistry ; Organic chemistry ; Human health ; Engineering Aerogels and thermal insulation ; TiO2 Photocatalysis and Solar Cells ; Luminescence Properties of Advanced Materials
Authors: Hengli Xiang | Genkuan Ren | Yanjun Zhong | Dehua Xu | Zhiye Zhang | Xin-Long Wang | Xiushan Yang
Source: Nanomaterials (article, 2021-01-27)
Field: Chemistry — Organic Chemistry

Fe3O4@C Nanoparticles Synthesized by In Situ Solid-Phase Method for Removal of Methylene Blue Fe<sub>3</sub>O<sub>4</sub>@C nanoparticles were prepared by an in situ, solid-phase reaction, without any precursor, using FeSO<sub>4</sub>, FeS<sub>2</sub>, and PVP K30 as raw materials. The nanoparticles were utilized to decolorize high concentrations methylene blue (MB). The results indicated that the maximum adsorption capacity of the Fe3O4@C nanoparticles was 18.52 mg/g, and that the adsorption process was exothermic. Additionally, by employing H<sub>2</sub>O<sub>2</sub> as the initiator of a Fenton-like reaction, the removal efficiency of 100 mg/L MB reached ~99% with Fe<sub>3</sub>O<sub>4</sub>@C nanoparticles, while that of MB was only ~34% using pure Fe<sub>3</sub>O<sub>4</sub> nanoparticles. The mechanism of H<sub>2</sub>O<sub>2</sub> activated on the Fe<sub>3</sub>O<sub>4</sub>@C nanoparticles and the possible degradation pathways of MB are discussed. The Fe<sub>3</sub>O<sub>4</sub>@C nanoparticles retained high catalytic activity after five usage cycles. This work describes a facile method for producing Fe<sub>3</sub>O<sub>4</sub>@C nanoparticles with excellent catalytic reactivity, and therefore, represents a promising approach for the industrial production of Fe<sub>3</sub>O<sub>4</sub>@C nanoparticles for the treatment of high concentrations of dyes in wastewater. Methylene blue ; Nanoparticle ; Catalysis ; Adsorption ; Exothermic reaction ; Reactivity (psychology) ; Chemical engineering ; Materials science ; Degradation (telecommunications) ; Phase (matter) ; Nuclear chemistry ; Chemistry ; Nanotechnology ; Organic chemistry ; Photocatalysis Nanomaterials for catalytic reactions ; Environmental remediation with nanomaterials ; Adsorption and biosorption for pollutant removal
Authors: L. Natrayan | Anjibabu Merneedi | Dhinakaran Veeman | S. Kaliappan | P. Satyanarayana Raju | Ram Subbiah | S. Venkatesh Kumar
Source: Journal of Nanomaterials (article, 2021-11-08)
Field: Materials Science — Materials Chemistry

Evaluating the Mechanical and Tribological Properties of DLC Nanocoated Aluminium 5051 Using RF Sputtering The diamond-like carbon- (DLC-) coating technique is used in the sliding parts of automotive engines, among other applications, to reduce friction and wear. In this work, DLC has been coated on the Aluminium 5051 sample to assess the mechanical and tribological properties. A sputtering deposition mechanism is used, and the DLC is coated using a graphite target. The developed DLC coatings are tested for adhesion strength, hardness, chemical composition using XRD, and wear behaviour. The developed DLC thin films have considerably increased the wear behaviour of the Aluminium 5051 sample and have fulfilled the objective of this study. The XRD data indicated the presence of amorphous carbon in the coating with a threefold increase to the hardness of the naked aluminium. This study provides insight into improving the aluminium wear resistance by developing a considerably hard coating. Materials science ; Tribology ; Aluminium ; Coating ; Diamond-like carbon ; Graphite ; Sputter deposition ; Metallurgy ; Sputtering ; Composite material ; Carbon fibers ; Deposition (geology) ; Adhesion ; Amorphous carbon ; Amorphous solid ; Thin film ; Nanotechnology ; Composite number Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Tribology and Wear Analysis
Authors: Stephen J. McPhail | S. Frangini | Jérôme Laurencin | E. Effori | Amira Abaza | Aiswarya Krishnakumar Padinjarethil | Anke Hagen | Aline Léon | Annabelle Brisse | Daria Vladikova | Blagoy Burdin | Fiammetta Rita Bianchi | Bárbara Bosio | Paolo Piccardo | Roberto Spotorno | Hiroyuki Uchida | Pierpaolo Polverino | Ennio Andrea Adinolfi | F. Postiglione | Jong‐Ho Lee | Hamza Moussaoui | Jan Van herle
Source: Electrochemical Science Advances (review, 2021-11-23)
Field: Materials Science — Materials Chemistry

Addressing planar solid oxide cell degradation mechanisms: A critical review of selected components Abstract In this review paper, a critical assessment of the main degradation processes in three key components of solid oxide fuel cells and electrolysers (negative and positive electrodes and the interconnect) is undertaken, attempting prioritization of respective degradation effects and recommendation of the best approaches in their experimental ascertainment and numerical modeling. Besides different approaches to quantifying the degradation rate of an operating solid oxide cell (SOC), the latest advancements in microstructural representation (3D imaging and reconstruction) of SOC electrodes are reviewed, applied to the quantification of triple‐phase boundary (TPB) lengths and morphology evolution over time. The intrinsic degradation processes in the negative (fuel) electrode and the positive (oxygen) electrode are discussed, covering first the composition and governing mechanisms of the respective electrodes, followed by a comprehensive evaluation of the most important factors of degradation during operation. By this systematic identification of dominant degradation processes, measurement techniques, and modeling approaches, the foundations are laid for the definition of meaningful accelerated stress testing of SOC cells and stacks, which will help the technology achieve the constantly more demanding durability targets in market applications. Degradation (telecommunications) ; Electrode ; Solid oxide fuel cell ; Materials science ; Computer science ; Oxide ; Durability ; Prioritization ; Triple phase boundary ; Fuel cells ; Process engineering ; Stress (linguistics) ; Representation (politics) ; Reliability engineering ; Biological system ; Chemical engineering ; Anode ; Composite material ; Chemistry ; Engineering Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Nuclear Materials and Properties
Authors: Dário Passos | Puneet Mishra
Source: Chemometrics and Intelligent Laboratory Systems (article, 2021-06-04)
Field: Chemistry — Analytical Chemistry

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling In deep learning (DL) modelling for spectral data, a major challenge is related to the choice of DL network architecture and the selection of the best hyperparameters. Often, slight changes to the neural architecture or its hyperparameter can have a direct influence on the model's performance, making its robustness questionable. To deal with it, this study presents an automated deep learning modelling based on advanced optimisation techniques involving Hyperband and Bayesian optimisation, to automatically find optimal neural architecture and its hyperparameters to reach robust DL models. The optimisation requires a base neural architecture to be initialized, however, later it automatically adjusts the neural architecture and the hyperparameters to reach the optimal model. Furthermore, to support the interpretation of the DL models, a wavelength weighing schema based on gradient-weighted class activation mapping (Grad-CAM) was implemented. The potential of the approach was showed on a real case of wheat variety classification with near-infrared spectral data. The performance of the classification was compared with that previously reported on the same dataset with different DL and chemometric approaches. The results showed that with the proposed approach a classification accuracy of 94.9% was reached, which was better than the best reported accuracy on the same data set i.e., 93%. Furthermore, the better performance was obtained with a simpler neural architecture compared to what was used in earlier studies. The automated deep learning based on advanced optimisation can support DL modelling of spectral data. Hyperparameter ; Computer science ; Artificial intelligence ; Artificial neural network ; Machine learning ; Deep learning ; Robustness (evolution) ; Model selection ; Pattern recognition (psychology) ; Data mining Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Marianna Kotzabasaki | Iason Sotiropoulos | Costas A. Charitidis | Haralambos Sarimveis
Source: Nanoscale Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction Multi-walled carbon nanotubes (MWCNTs) are made of multiple single-walled carbon nanotubes (SWCNTs) which are nested inside one another forming concentric cylinders. These nanomaterials are widely used in industrial and biomedical applications, due to their unique physicochemical characteristics. However, previous studies have shown that exposure to MWCNTs may lead to toxicity and some of the physicochemical properties of MWCNTs can influence their toxicological profiles. <i>In silico</i> modelling can be applied as a faster and less costly alternative to experimental (<i>in vivo</i> and <i>in vitro</i>) testing for the hazard characterization of MWCNTs. This study aims at developing a fully validated predictive nanoinformatics model based on statistical and machine learning approaches for the accurate prediction of genotoxicity of different types of MWCNTs. Towards this goal, a number of different computational workflows were designed, combining unsupervised (Principal Component Analysis, PCA) and supervised classification techniques (Support Vectors Machine, "SVM", Random Forest, "RF", Logistic Regression, "LR" and Naïve Bayes, "NB") and Bayesian optimization. The Recursive Feature Elimination (RFE) method was applied for selecting the most important variables. An RF model using only three features was selected as the most efficient for predicting the genotoxicity of MWCNTs, exhibiting 80% accuracy on external validation and high classification probabilities. The most informative features selected by the model were "Length", "Zeta average" and "Purity". Carbon nanotube ; Genotoxicity ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Carbon Nanotubes in Composites ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Md. Mahabubur Rahman | Lway Faisal Abdulrazak | Mominul Ahsan | Md. Abdul Based | Md. Masud Rana | Md. Shamim Anower | Khaleda Akter Rikta | Julfikar Haider | Gurusamy Saravanakumar
Source: IEEE Access (article, 2021-12-22)
Field: Materials Science — Materials Chemistry

2D Nanomaterial-Based Hybrid Structured (Au-WSe<sub>2</sub>-PtSe<sub>2</sub>-BP) Surface Plasmon Resonance (SPR) Sensor With Improved Performance As a promising optical method used in a variety of applications surface plasmon resonance (SPR) sensors are employed over a wide range of boundaries. This research proposes a highly sensitive SPR based sensor with a novel hybrid structure using transition metal dichalcogenides (e.g. WSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , PtSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) along with black phosphorene (BP) through comprehensive numerical study. To analyze and evaluate the performances of the proposed sensor, the widely used transfer matrix method (TMM) was used. The performances of the sensor were measured in terms of reflectivity, sensitivity, detection accuracy (DA), and figure of merit (FOM). The sensor structure was optimized by changing different structural parameters of the hybrid architecture to obtain better performances. The results revealed that insertion of PtSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> with WSe <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and BP over a gold layer of the conventional structure improved the performance of the sensor and the maximum sensitivity of the sensor was measured as 200 deg/RIU with a FOM of 17.70 RIU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> . As well, the light penetration through the optimized sensor is investigated using the finite element method (FEM) based software. With this kind of high sensing capabilities, it may be convinced that the proposed sensor can be applied in different fields of biosensing to detect liquid biological and biochemical samples or analytes. Surface plasmon resonance ; Figure of merit ; Materials science ; Computer science ; Optoelectronics ; Nanotechnology ; Nanoparticle 2D Materials and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Van‐An Duong | Jong‐Moon Park | Hee–Joung Lim | Hookeun Lee
Source: Applied Sciences (article, 2021-04-09)
Field: Chemistry — Spectroscopy

Proteomics in Forensic Analysis: Applications for Human Samples Proteomics, the large-scale study of all proteins of an organism or system, is a powerful tool for studying biological systems. It can provide a holistic view of the physiological and biochemical states of given samples through identification and quantification of large numbers of peptides and proteins. In forensic science, proteomics can be used as a confirmatory and orthogonal technique for well-built genomic analyses. Proteomics is highly valuable in cases where nucleic acids are absent or degraded, such as hair and bone samples. It can be used to identify body fluids, ethnic group, gender, individual, and estimate post-mortem interval using bone, muscle, and decomposition fluid samples. Compared to genomic analysis, proteomics can provide a better global picture of a sample. It has been used in forensic science for a wide range of sample types and applications. In this review, we briefly introduce proteomic methods, including sample preparation techniques, data acquisition using liquid chromatography-tandem mass spectrometry, and data analysis using database search, spectral library search, and de novo sequencing. We also summarize recent applications in the past decade of proteomics in forensic science with a special focus on human samples, including hair, bone, body fluids, fingernail, muscle, brain, and fingermark, and address the challenges, considerations, and future developments of forensic proteomics. Proteomics ; Computational biology ; Computer science ; Identification (biology) ; Data science ; Biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Indy Magnus | Martin Virte | Hugo Thienpont | Lien Smeesters
Source: Food Control (article, 2021-06-29)
Field: Chemistry — Analytical Chemistry

Combining optical spectroscopy and machine learning to improve food classification Artificial intelligence ; Computer science ; Classifier (UML) ; Feature selection ; Support vector machine ; Pattern recognition (psychology) ; Extreme learning machine ; Machine learning ; Cascade ; Data mining ; Chemistry ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Hamid Rajabalipanah | Ali Abdolali | Shahid Iqbal | Lei Zhang | Tie Jun Cui
Source: Nanophotonics (article, 2021-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analog signal processing through space‐time digital metasurfaces Abstract In the quest to realize analog signal processing using subwavelength metasurfaces, in this paper, we present the first demonstration of programmable time‐modulated metasurface processors based on the key properties of spatial Fourier transformation. Exploiting space‐time coding strategy enables local, independent, and real‐time engineering of not only amplitude but also phase profile of the contributing reflective digital meta‐atoms at both central and harmonic frequencies. Several illustrative examples are demonstrated to show that the proposed multifunctional calculus metasurface is capable of implementing a large class of useful mathematical operators, including 1st‐ and 2nd‐order spatial differentiation, 1st‐order spatial integration, and integro‐differential equation solving accompanied by frequency conversions. Unlike the recent proposals based on the Green’s function (GF) method, the designed time‐modulated signal processor effectively operates for input signals containing wide spatial frequency bandwidths with an acceptable gain level. Proof‐of‐principle simulations are also reported to demonstrate the successful realization of image processing functions like edge detection. This time‐varying wave‐based computing system can set the direction for future developments of programmable metasurfaces with highly promising applications in ultrafast equation solving, real‐time and continuous signal processing, and imaging. Computer science ; Analog signal processing ; Signal processing ; Fourier transform ; Digital signal processing ; Realization (probability) ; SIGNAL (programming language) ; Electronic engineering ; Computational science ; Physics ; Mathematics ; Computer hardware ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Radio Wave Propagation Studies