jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dou Li | Xiao‐Lei Shi | Jiaxi Zhu | Meng Li | Jianyuan Wang | Wei‐Di Liu | Qinghua Zhao | Hong Zhong | Shuangming Li | Zhi‐Gang Chen
Source: Advanced Energy Materials (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Ce‐filled Ni<sub>1.5</sub>Co<sub>2.5</sub>Sb<sub>12</sub> Skutterudite Thin Films with Record‐High Figure of Merit And Device Performance Abstract Realizing high thermoelectric performance in CoSb 3 skutterudite‐based thin films and their devices is historically challenging, especially due to the lack of high‐performing thin‐film‐based device working at medium‐to‐high temperatures. Here, a record‐high ZT of 1.1 is achieved at 683 K in an n‐type Ce 0.3 Ni 1.5 Co 2.5 Sb 12 thin film, fabricated from a self‐designed target via advanced pulsed laser deposition. Both experimental and computational results confirm that the Ce‐filling and metal‐featured nanoinclusions such as CeSb contribute to high electrical conductivity, while the Ni‐doping and significantly strengthen the energy filtering effect that occurs at the dense interfaces between the Ce 0.3 Ni 1.5 Co 2.5 Sb 12 matrix and the nanoinclusions which leads to a large Seebeck coefficient, giving rise to such a high ZT . In addition, a new‐type CoSb 3 thin‐film‐based device is successfully fabricated, which exhibits a high output power density of 8.25 mW cm −2 at a temperature difference of 140 K and a cold‐side temperature of 573 K, indicating the potential for application to medium‐to‐high‐temperature power generation scenarios. Skutterudite ; Materials science ; Thin film ; Thermoelectric effect ; Pulsed laser deposition ; Seebeck coefficient ; Figure of merit ; Optoelectronics ; Thermoelectric materials ; Thermal conductivity ; Doping ; Electrical resistivity and conductivity ; Composite material ; Nanotechnology ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Chalcogenide Semiconductor Thin Films
Authors: Julie Horne | Charlotte De Bleye | Pierre Lebrun | Kevser Kemik | Thomas Van Laethem | Pierre‐Yves Sacré | Philippe Hubert | Cédric Hubert | Eric Ziémons
Source: Journal of Pharmaceutical and Biomedical Analysis (review, 2023-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimization of silver nanoparticles synthesis by chemical reduction to enhance SERS quantitative performances: Early characterization using the quality by design approach Surface-enhanced Raman scattering (SERS) is a vibrational widely used technique thanks to its multiple advantages such as its high specificity and sensitivity. The Raman signal exaltation comes from the use of metallic nanoparticles (Nps) acting as antennas by amplifying the Raman scattering. Controlling the Nps synthesis is a major point for the implementation of SERS in routine analysis and especially in quantitative applications. Effectively, nature, size and shape of these Nps considerably influence the SERS response intensity and repeatability. The Lee-Meisel protocol is the most common synthesis route used by the SERS community due to the low cost, rapidity and ease of manufacturing. However, this process leads to a significant heterogeneity in terms of particle size and shape. In this context, this study aimed to synthesize repeatable and homogeneous silver nanoparticles (AgNps) by chemical reduction. The Quality by Design strategy from quality target product profile to early characterization design was considered to optimize this reaction. The first step of this strategy aimed to highlight critical parameters by the means of an early characterization design. Based on an Ishikawa diagram, five process parameters were studied: the reaction volume as categorical variable and the temperature, the time of reaction, the trisodium citrate concentration and pH as continuous variables. A D-Optimal design of 35 conditions was performed. Three critical quality attributes were selected to maximize the SERS intensity, minimize the variation coefficient on SERS intensities and the polydispersity index of the AgNps. Considering these factors, it appeared that concentration, pH and time of reaction were identified as having a critical impact on the Nps formation and can then be considered for the further optimization step. Chemistry ; Raman scattering ; Dispersity ; Context (archaeology) ; Repeatability ; Raman spectroscopy ; Nanotechnology ; Nanoparticle ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Optics ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticles: synthesis and applications ; Quantum Dots Synthesis And Properties
Authors: В. И. Тимофеев | В. Р. Самыгина
Source: Crystals (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Protein Crystallography: Achievements and Challenges Proteins are the most important biological macromolecules, and are involved in almost all aspects of life. Therefore, the study of the structure of proteins is of great practical and fundamental importance. On the one hand, knowledge of the spatial structure is necessary to study the basic principles of protein functioning; for example, the mechanisms of enzymatic reactions. On the other hand, knowledge of the spatial structure of proteins is used, for example, in biotechnology, for the design of enzymes with desired properties, as well as in drug design. Today, the main method for determining the spatial structure of a protein is X-ray structural analysis of protein crystals. The main difficulty in applying this method is in obtaining a perfect protein-crystal. This review is devoted to the successes and challenges of modern protein crystallography. Protein structure ; Protein crystallization ; Computational biology ; Structural biology ; Computer science ; Chemistry ; Biochemistry ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Porphyrin Metabolism and Disorders
Authors: Yulong Li | Nikolay Garabedian | Johannes Schneider | Christian Greiner
Source: Tribology Letters (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Waviness Affects Friction and Abrasive Wear Abstract Abrasive wear can have a detrimental effect on machinery, especially in the mining and construction industries. To prolong machinery lifetime and cut down energy consumption, a thorough understanding of abrasive wear is essential: surface topography measurement and interpretation (including form, waviness, and roughness) are vitally important. However, the potentially crucial influence of surface topography intricacies on tribological behavior has been obscured since roughness and waviness are considered simple scalar quantities in most cases (e.g., roughness R a and waviness W t ). In this work, the complete waviness profile of the sliding track was used to shed light on the influence of surface topography on abrasive wear. Bearing steel (100Cr6, AISI 52100) pins and disks were tribologically tested in a flat-on-flat contact with Al 2 O 3 -based slurries as interfacial medium. Using slurries with two different particle sizes, 5 and 13 μm, we found that friction fluctuates only with small abrasive particles (5-µm slurry) and relatively low waviness disks. It was found that even small surface deviations (albeit minimized and controlled for) can significantly increase the friction coefficient—up to 91%. Remarkably, not only are frictional fluctuations strongly correlated with the disk’s initial waviness profile, but these small fluctuations correlate with unevenly distributed high wear. These findings enhance our understanding of the friction wear structure and provide the basis for exploring how surfaces can be optimized for better tribological performance. Graphical Abstract Waviness ; Abrasive ; Materials science ; Tribology ; Surface finish ; Surface roughness ; Slurry ; Composite material ; Particle (ecology) ; Metallurgy ; Geology Metal Alloys Wear and Properties ; Adhesion, Friction, and Surface Interactions ; Tribology and Wear Analysis
Authors: Maimunah Mohd Ali | Norhashila Hashim | Samsuzana Abd Aziz | Ola Lasekan
Source: Agronomy (article, 2023-01-30)
Field: Chemistry — Analytical Chemistry

Utilisation of Deep Learning with Multimodal Data Fusion for Determination of Pineapple Quality Using Thermal Imaging Fruit quality is an important aspect in determining the consumer preference in the supply chain. Thermal imaging was used to determine different pineapple varieties according to the physicochemical changes of the fruit by means of the deep learning method. Deep learning has gained attention in fruit classification and recognition in unimodal processing. This paper proposes a multimodal data fusion framework for the determination of pineapple quality using deep learning methods based on the feature extraction acquired from thermal imaging. Feature extraction was selected from the thermal images that provided a correlation with the quality attributes of the fruit in developing the deep learning models. Three different types of deep learning architectures, including ResNet, VGG16, and InceptionV3, were built to develop the multimodal data fusion framework for the classification of pineapple varieties based on the concatenation of multiple features extracted by the robust networks. The multimodal data fusion coupled with powerful convolutional neural network architectures can remarkably distinguish different pineapple varieties. The proposed multimodal data fusion framework provides a reliable determination of fruit quality that can improve the recognition accuracy and the model performance up to 0.9687. The effectiveness of multimodal deep learning data fusion and thermal imaging has huge potential in monitoring the real-time determination of physicochemical changes of fruit. Deep learning ; Artificial intelligence ; Concatenation (mathematics) ; Convolutional neural network ; Computer science ; Feature extraction ; Machine learning ; Fusion ; Pattern recognition (psychology) ; Deep belief network ; Quality (philosophy) ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Qiongda Zhong | Zhang Hu | Shuqi Tang | Peng Li | Caixia Lin | Ling Zhang | Nan Zhong
Source: Foods (article, 2023-05-22)
Field: Chemistry — Analytical Chemistry

Feasibility Study of Combining Hyperspectral Imaging with Deep Learning for Chestnut-Quality Detection The rapid detection of chestnut quality is a critical aspect of chestnut processing. However, traditional imaging methods pose a challenge for chestnut-quality detection due to the absence of visible epidermis symptoms. This study aims to develop a quick and efficient detection method using hyperspectral imaging (HSI, 935-1720 nm) and deep learning modeling for qualitative and quantitative identification of chestnut quality. Firstly, we used principal component analysis (PCA) to visualize the qualitative analysis of chestnut quality, followed by the application of three pre-processing methods to the spectra. To compare the accuracy of different models for chestnut-quality detection, traditional machine learning models and deep learning models were constructed. Results showed that deep learning models were more accurate, with FD-LSTM achieving the highest accuracy of 99.72%. Moreover, the study identified important wavelengths for chestnut-quality detection at around 1000, 1400 and 1600 nm, to improve the efficiency of the model. The FD-UVE-CNN model achieved the highest accuracy of 97.33% after incorporating the important wavelength identification process. By using the important wavelengths as input for the deep learning network model, recognition time decreased on average by 39 s. After a comprehensive analysis, FD-UVE-CNN was deter-mined to be the most effective model for chestnut-quality detection. This study suggests that deep learning combined with HSI has potential for chestnut-quality detection, and the results are encouraging. Hyperspectral imaging ; Artificial intelligence ; Deep learning ; Computer science ; Principal component analysis ; Pattern recognition (psychology) ; Quality (philosophy) ; Machine learning Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Advanced Chemical Sensor Technologies
Authors: Daniela Cotta Ribeiro | Habib Asseiss Neto | Juliana S. Lima | Débora Cristina Sampaio de Assis | Kelly Moura Keller | Sérgio Campos | Daniel A. Oliveira | L.M. Fonseca
Source: Heliyon (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Determination of the lactose content in low-lactose milk using Fourier-transform infrared spectroscopy (FTIR) and convolutional neural network Demand for low lactose milk and milk products has been increasing worldwide due to the high number of people with lactose intolerance. These low lactose dairy foods require fast, low-cost and efficient methods for sugar quantification. However, available methods do not meet all these requirements. In this work, we propose the association of FTIR (Fourier Transform Infrared) spectroscopy with artificial intelligence to identify and quantify residual lactose and other sugars in milk. Convolutional neural networks (CNN) were built from the infrared spectra without preprocessing the data using hyperparameter adjustment and saliency map. For the quantitative prediction of the sugars in milk, a regression model was proposed, while for the qualitative assessment, a classification model was used. Raw, pasteurized and ultra-high temperature (UHT) milk was added with lactose, glucose, and galactose in six concentrations (0.1-7.0 mg mL<sup>-1</sup>) and, in total, 432 samples were submitted to convolutional neural network. Accuracy, precision, sensitivity, specificity, root mean square error, mean square error, mean absolute error, and coefficient of determination (R<sup>2</sup>) were used as evaluation parameters. The algorithms indicated a predictive capacity (accuracy) above 95% for classification, and R<sup>2</sup> of 81%, 86%, and 92% for respectively, lactose, glucose, and galactose quantification. Our results showed that the association of FTIR spectra with artificial intelligence tools, such as CNN, is an efficient, quick, and low-cost methodology for quantifying lactose and other sugars in milk. Lactose ; Galactose ; Food science ; Mean squared error ; Chemistry ; Fourier transform infrared spectroscopy ; Artificial intelligence ; Sugar ; Mathematics ; Pasteurization ; Correlation coefficient ; Artificial neural network ; Chemometrics ; Chromatography ; Pattern recognition (psychology) ; Computer science ; Statistics ; Biochemistry ; Engineering Spectroscopy and Chemometric Analyses ; Probiotics and Fermented Foods ; Advanced Chemical Sensor Technologies
Authors: Kamil Szpotkowski | Klaudia Wójcik | Anna Kurzyńska‐Kokorniak
Source: Computational and Structural Biotechnology Journal (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Structural studies of protein–nucleic acid complexes: A brief overview of the selected techniques Protein-nucleic acid complexes are involved in all vital processes, including replication, transcription, translation, regulation of gene expression and cell metabolism. Knowledge of the biological functions and molecular mechanisms beyond the activity of the macromolecular complexes can be determined from their tertiary structures. Undoubtably, performing structural studies of protein-nucleic acid complexes is challenging, mainly because these types of complexes are often unstable. In addition, their individual components may display extremely different surface charges, causing the complexes to precipitate at higher concentrations used in many structural studies. Due to the variety of protein-nucleic acid complexes and their different biophysical properties, no simple and universal guideline exists that helps scientists chose a method to successfully determine the structure of a specific protein-nucleic acid complex. In this review, we provide a summary of the following experimental methods, which can be applied to study the structures of protein-nucleic acid complexes: X-ray and neutron crystallography, nuclear magnetic resonance (NMR) spectroscopy, cryogenic electron microscopy (cryo-EM), atomic force microscopy (AFM), small angle scattering (SAS) methods, circular dichroism (CD) and infrared (IR) spectroscopy. Each method is discussed regarding its historical context, advancements over the past decades and recent years, and weaknesses and strengths. When a single method does not provide satisfactory data on the selected protein-nucleic acid complex, a combination of several methods should be considered as a hybrid approach; thus, specific structural problems can be solved when studying protein-nucleic acid complexes. Nucleic acid ; Nucleic acid structure ; Context (archaeology) ; Chemistry ; Biophysics ; Structural biology ; Nuclear magnetic resonance spectroscopy ; Biochemistry ; Crystallography ; RNA ; Biology ; Gene ; Stereochemistry Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Bacteriophages and microbial interactions
Authors: Fu Zhang | Fangyuan Zhang | Shunqing Wang | Lantao Li | Qiang Lv | Sanling Fu | Xinyue Wang | Qingfeng Lv | Yakun Zhang
Source: Frontiers in Plant Science (article, 2023-09-08)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging combined with CNN for maize variety identification The results show that the maize variety classification model works best, when the input feature dimension is 768 and the layer depth factor d is 1.0. At this point, the model accuracy of the test set is 96.65% and the detection frame rate is1000 Fps/s in GPU environment, which can realize the rapid and effective non-destructive detection of maize varieties. This study provides a new idea for the rapid and accurate identification of maize seeds and seeds of other crops. Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Normalization (sociology) ; Feature (linguistics) ; Smoothing ; Computer science ; Convolutional neural network ; Projection (relational algebra) ; Mathematics ; Computer vision ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Matilde Concilio | Ramón Garcia Maset | Laia Pasquina-Lemonche | Vito Kontrimas | Ji‐Inn Song | Santhosh Kalash Rajendrakumar | Freya Harrison | C. Remzi Becer | Sébastien Perrier
Source: Advanced Healthcare Materials (article, 2023-07-31)
Field: Chemistry — Organic Chemistry

Mechanism of Action of Oxazoline‐Based Antimicrobial Polymers Against <i>Staphylococcus aureus</i>: In Vivo Antimicrobial Activity Evaluation Antimicrobial-resistant pathogens have reached alarming levels, becoming one of the most pressing global health issues. Hence, new treatments are necessary for the fight against antimicrobial resistance. Synthetic nanoengineered antimicrobial polymers (SNAPs) have emerged as a promising alternative to antimicrobial peptides, overcoming some of their limitations while keeping their key features. Herein, a library of amphiphilic oxazoline-based SNAPs using cationic ring-opening polymerization (CROP) is designed. Amphipathic compounds with 70% cationic content exhibit the highest activity against clinically relevant Staphylococcus aureus isolates, maintaining good biocompatibility in vitro and in vivo. The mechanism of action of the lead compounds against S. aureus is assessed using various microscopy techniques, indicating cell membrane disruption, while the cell wall remains unaffected. Furthermore, a potential interaction of the compounds with bacterial DNA is shown, with possible implications on bacterial division. Finally, one of the compounds exhibits high efficacy in vivo in an insect infection model. Antimicrobial ; Staphylococcus aureus ; In vivo ; Amphiphile ; Microbiology ; Chemistry ; Mechanism of action ; Bacteria ; Biology ; In vitro ; Biochemistry ; Polymer ; Biotechnology ; Organic chemistry ; Copolymer Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Click Chemistry and Applications
Authors: Thamer Diwan | Zaidun Naji Abudi | Mustafa H. Al‐Furaiji | Arian Nijmeijer
Source: Membranes (article, 2023-04-28)
Field: Materials Science — Biomaterials

A Competitive Study Using Electrospinning and Phase Inversion to Prepare Polymeric Membranes for Oil Removal Polyacrylonitrile (PAN) is a popular polymer that can be made into membranes using various techniques, such as electrospinning and phase inversion. Electrospinning is a novel technique that produces nonwoven nanofiber-based membranes with highly tunable properties. In this research, electrospun PAN nanofiber membranes with various concentrations (10, 12, and 14% PAN/dimethylformamide (DMF)) were prepared and compared to PAN cast membranes prepared by the phase inversion technique. All of the prepared membranes were tested for oil removal in a cross-flow filtration system. A comparison between these membranes' surface morphology, topography, wettability, and porosity was presented and analyzed. The results showed that increasing the concentration of the PAN precursor solution increases surface roughness, hydrophilicity, and porosity and, consequently, enhances the membrane performance. However, the PAN cast membranes showed a lower water flux when the precursor solution concentration increased. In general, the electrospun PAN membranes performed better in terms of water flux and oil rejection than the cast PAN membranes. The electrospun 14% PAN/DMF membrane gave a water flux of 250 LMH and a rejection of 97% compared to the cast 14% PAN/DMF membrane, which showed a water flux of 117 LMH and 94% oil rejection. This is mainly because the nanofibrous membrane showed higher porosity, higher hydrophilicity, and higher surface roughness compared to the cast PAN membranes at the same polymer concentration. The porosity of the electrospun PAN membrane was 96%, while it was 58% for the cast 14% PAN/DMF membrane. Membrane ; Polyacrylonitrile ; Electrospinning ; Phase inversion ; Materials science ; Chemical engineering ; Nanofiber ; Porosity ; Contact angle ; Wetting ; Polymer chemistry ; Polymer ; Surface roughness ; Composite material ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Surface Modification and Superhydrophobicity