jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinliang Feng | Yanqiu Xia | Peiyuan Xie | Xiaohe Li
Source: Friction (article, 2023-12-05)
Field: Chemistry — Analytical Chemistry

Classification and spectrum optimization method of grease based on infrared spectrum Abstract The infrared (IR) absorption spectral data of 63 kinds of lubricating greases containing six different types of thickeners were obtained using the IR spectroscopy. The Kohonen neural network algorithm was used to identify the type of the lubricating grease. The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. The 63 kinds of greases exhibit spatial clustering under certain IR spectrum recognition spectral bands, which are linked to characteristic peaks of lubricating greases and improve the recognition accuracy of these greases. The model achieved recognition accuracy of 100.00%, 96.08%, 94.87%, 100.00%, and 87.50% for polyurea grease, calcium sulfonate composite grease, aluminum (Al)-based grease, bentonite grease, and lithium-based grease, respectively. Based on the different IR absorption spectrum bands produced by each kind of lubricating grease, the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy. This paper demonstrates fast recognition speed and high accuracy, proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease. Grease ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Infrared spectroscopy ; Artificial neural network ; Computer science ; Self-organizing map ; Biological system ; Composite material ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Gear and Bearing Dynamics Analysis
Authors: Neha Varshney | Abhinava K. Mishra
Source: Proteomes (review, 2023-05-02)
Field: Chemistry — Spectroscopy

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer. Phosphoproteomics ; Drug discovery ; Proteomics ; Context (archaeology) ; Computational biology ; Computer science ; Phosphorylation ; Protein phosphorylation ; Bioinformatics ; Data science ; Biology ; Protein kinase A ; Biochemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Genomics and Phylogenetic Studies
Authors: Yasser Boucenna | Abdelheq Layachi | Abdelhakim Cherfia | Fouad Laoutid | Hamid Satha
Source: Materials (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

Non-Isothermal Crystallization Kinetics and Activation Energy for Crystal Growth of Polyamide 66/Short Glass Fiber/Carbon Black Composites This study presents the effect of the addition of 0.4 wt.% carbon black (CB) to polyamide 66 (PA66) containing 30 wt.% short glass fibers (GFs) on the behavior of composite thermal crystallization. Composites were studied by differential scanning calorimetry analysis (DSC) at different cooling rates using wide-angle X-ray scattering (WAXS) and scanning electron microscopy (SEM). This thermal crystallization study highlights the nucleation effect of GFs that promote PA66 crystallization by significantly increasing crystallization kinetics and rates. The activation energies (Eas) calculated by model-free (FWO; KAS) and model-fitting (Kissinger method and C-R method) approaches showed that the combination of both GF and CB decreases the activation energy with respect to neat PA66, meaning that the presence of both additives facilitates crystallization. The Coats-Redfern and Criado methods showed that the crystallization of neat PA66 and related composites follows the second-order reaction, i.e., the decelerated reaction, evidencing compatibility between GFs and the matrix. Materials science ; Composite material ; Activation energy ; Crystallization ; Polyamide ; Kinetics ; Isothermal process ; Glass fiber ; Fiber ; Crystal growth ; Carbon black ; Crystal (programming language) ; Chemical engineering ; Chemistry ; Crystallography ; Thermodynamics ; Physical chemistry Thermal and Kinetic Analysis ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: Wei Liu | Vsevolod Ivanov | Kaushalya Jhuria | Qing Ji | Arun Persaud | Walid Redjem | Jacopo Simoni | Yertay Zhiyenbayev | Boubacar Kanté | J. Garcı́a López | Liang Z. Tan | T. Schenkel
Source: Physical Review Applied (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

Quantum Emitter Formation Dynamics and Probing of Radiation-Induced Atomic Disorder in Silicon Near-infrared color centers in silicon are emerging candidates for on-chip integrated quantum emitters, optical-access quantum memories, and sensing. We access ensemble G-color-center formation dynamics and radiation-induced atomic disorder in silicon for a series of megaelectronvolt proton-flux conditions. The photoluminescence results reveal that the G centers are formed more efficiently by pulsed-proton irradiation than by continuous-wave proton irradiation. The enhanced transient excitations and dynamic annealing within nanoseconds allows optimization of the ratio of G-center formation to nonradiative defect accumulation. The G centers preserve narrow line widths of about 0.1 nm when they are generated by moderate pulsed-proton fluences, while the line width broadens significantly as the pulsed-proton fluence increases. This implies vacancy or interstitial clustering by overlapping collision cascades. The tracking of G-center properties for a series of irradiation conditions enables sensitive probing of atomic disorder, serving as a complementary analytical method for sensing damage accumulation. Aided by ab initio electronic structure calculations, we provide insight into the atomic disorder induced inhomogeneous broadening by introducing vacancies, silicon interstitials, and oriented strain fields in the vicinity of a G center. A vacancy leads to a tensile strain and can result in either a red shift or a blue shift of the G-center emission, depending on its position relative to the G center. Meanwhile, $\mathrm{Si}$ interstitials lead to compressive strain, which results in a monotonic red shift. High-flux and tunable ion pulses enable the exploration of the fundamental dynamics of radiation-induced defects as well as methods for the optimization of G-center formation and qubit synthesis for quantum information processing. Materials science ; Silicon ; Proton ; Irradiation ; Fluence ; Atom (system on chip) ; Vacancy defect ; Atomic physics ; Quantum dot ; Photoluminescence ; Molecular physics ; Optoelectronics ; Condensed matter physics ; Physics Silicon Nanostructures and Photoluminescence ; Semiconductor materials and devices ; Nanowire Synthesis and Applications
Authors: Mengqing Qiu | Le Tang | Jinghong Wang | Qingshan Xu | Shouguo Zheng | Shizhuang Weng
Source: Foods (article, 2023-08-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm<sup>2</sup>, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field. Pyrene ; Substrate (aquarium) ; In situ ; Chemistry ; Raman spectroscopy ; Colloidal gold ; Nanoparticle ; Chromatography ; Materials science ; Environmental chemistry ; Nanotechnology ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Daniel Bell | Tongtong Zhang | Niklas Geue | Ciarán J. Rogers | Perdita E. Barran | Alice M. Bowen | Louise S. Natrajan | Imogen A. Riddell
Source: Chemistry - A European Journal (article, 2023-09-21)
Field: Materials Science — Materials Chemistry

Hexanuclear Ln<sub>6</sub>L<sub>6</sub> Complex Formation by Using an Unsymmetric Ligand Multinuclear, self-assembled lanthanide complexes present clear opportunities as sensors and imaging agents. Despite the widely acknowledged potential of this class of supramolecule, synthetic and characterization challenges continue to limit systematic studies into their self-assembly restricting the number and variety of lanthanide architectures reported relative to their transition metal counterparts. Here we present the first study evaluating the effect of ligand backbone symmetry on multinuclear lanthanide complex self-assembly. Replacement of a symmetric ethylene linker with an unsymmetric amide at the center of a homoditopic ligand governs formation of an unusual Ln<sub>6</sub> L<sub>6</sub> complex with coordinatively unsaturated metal centers. The choice of triflate as a counterion, and the effect of ionic radii are shown to be critical for formation of the Ln<sub>6</sub> L<sub>6</sub> complex. The atypical Ln<sub>6</sub> L<sub>6</sub> architecture is characterized using a combination of mass spectrometry, luminescence, DOSY NMR and EPR spectroscopy measurements. Luminescence experiments support clear differences between comparable Eu<sub>6</sub> L<sub>6</sub> and Eu<sub>2</sub> L<sub>3</sub> complexes, with relatively short luminescent lifetimes and low quantum yields observed for the Eu<sub>6</sub> L<sub>6</sub> structure indicative of non-radiative decay processes. Synthesis of the Gd<sub>6</sub> L<sub>6</sub> analogue allows three distinct Gd⋯Gd distance measurements to be extracted using homo-RIDME EPR experiments. Lanthanide ; Luminescence ; Ligand (biochemistry) ; Counterion ; Electron paramagnetic resonance ; Chemistry ; Trifluoromethanesulfonate ; Ionic bonding ; Crystallography ; Materials science ; Ion ; Nuclear magnetic resonance ; Organic chemistry ; Physics Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; Organometallic Complex Synthesis and Catalysis
Authors: Ravinder Bhattoo | Sayan Ranu | N. M. Anoop Krishnan
Source: Machine Learning Science and Technology (article, 2023-01-04)
Field: Materials Science — Materials Chemistry

Learning the dynamics of particle-based systems with Lagrangian graph neural networks Abstract Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics. However, traditional approaches require the knowledge of several abstract quantities such as the energy or force to infer the dynamics of these particles. Here, we present a framework, namely, Lagrangian graph neural network ( LGnn ), that provides a strong inductive bias to learn the Lagrangian of a particle-based system directly from the trajectory. We test our approach on challenging systems with constraints and drag— LGnn outperforms baselines such as feed-forward Lagrangian neural network ( Lnn ) with improved performance. We also show the zero-shot generalizability of the system by simulating systems two orders of magnitude larger than the trained one and also hybrid systems that are unseen by the model, a unique feature. The graph architecture of LGnn significantly simplifies the learning in comparison to Lnn with ∼25 times better performance on ∼20 times smaller amounts of data. Finally, we show the interpretability of LGnn , which directly provides physical insights on drag and constraint forces learned by the model. LGnn can thus provide a fillip toward understanding the dynamics of physical systems purely from observable quantities. Interpretability ; Lagrangian ; Computer science ; Physical system ; Graph ; Artificial neural network ; Generalizability theory ; Inductive bias ; Trajectory ; Artificial intelligence ; Constraint (computer-aided design) ; Observable ; Feature (linguistics) ; Theoretical computer science ; Mathematics ; Physics ; Applied mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Fuel Cells and Related Materials
Authors: Fahad M. Alminderej | Abuzar E. A. E. Albadri | Yassine El‐Ghoul | Wael A. El‐Sayed | Alaa M. Younis | Sayed M. Saleh
Source: Sustainability (article, 2023-07-03)
Field: Materials Science — Materials Chemistry

Sustainable and Green Synthesis of Carbon Nanofibers from Date Palm Residues and Their Adsorption Efficiency for Eosin Dye This work investigates the prospective usage of dried date palm residues for eosin Y and eosin B (ES-Y and ES-B) dye removal from an aqueous solution. A green synthesis route is utilized to prepare carbon nanofibers (CNFs) from date palm residues. We study the characteristics of carbon nanomaterials based on their composition and morphology. The characterization includes different types of instruments such as a Fourier-Transform Infrared Spectroscopy (FTIR), Brunauer Emmett Teller (BET), and Scanning Electron Microscopy (SEM). Batch mode experimentations are conducted and studied utilizing various significant factors such as the dose of the adsorbent, solution pH, contact time, and the initial quantity of eosin molecules as a pollutant. The dye adsorption capability improves with an increasing adsorbent dose of up to 40 mg of CNFs. The adsorption of dyes onto CNFs achieves equilibrium in around 60 h, whereas the optimal starting dye concentration in this study is 50 ppm. Further, to study the under-investigated toxic molecules’ adsorption process mechanism on the nanomaterials’ active sites, we introduce kinetic models involving pseudo-first-order, pseudo-second-order, and models based on intra-particle diffusion. Langmuir and Freundlich’s isotherms are considered to study the equilibrium isotherms, and the Langmuir isotherm model deals considerably with the attained experimentation results. Adsorption ; Freundlich equation ; Langmuir ; Fourier transform infrared spectroscopy ; Chemical engineering ; Eosin ; Langmuir adsorption model ; Eosin Y ; Aqueous solution ; Materials science ; Chemistry ; Nuclear chemistry ; Activated carbon ; Organic chemistry Carbon and Quantum Dots Applications ; Dye analysis and toxicity ; Graphene and Nanomaterials Applications
Authors: Dibyendu Roy | Samiran Samanta | Sumit Roy | Andrew Smallbone | Anthony Paul Roskilly
Source: Energy (article, 2023-06-25)
Field: Materials Science — Materials Chemistry

Multi-objective optimisation of a power generation system integrating solid oxide fuel cell and recuperated supercritical carbon dioxide cycle This article presents an advanced power generation system that integrates a solid oxide fuel cell (SOFC) module with a recuperated supercritical CO2 (s-CO2) cycle. The waste heat generated by the exhaust of the SOFC module is utilised to drive the s-CO2 cycle, resulting in enhanced energy efficiency. The performance of the system was investigated through thermodynamic and economic analyses and optimised using response surface methodology. The optimisation process focused on two objectives: maximising the energy efficiency of the integrated system and minimising the levelised cost of electricity. The study meticulously analysed the effects of important variables such as current density, fuel utilisation factor, and operating temperature of the fuel cell. The optimisation efforts yielded impressive results, achieving an energy efficiency of 64% and a levelised cost of electricity (LCOE) of 0.18£/kWh. The proposed system surpassed traditional natural gas-fuelled power plants in terms of efficiency and specific emissions. Furthermore, the system's performance was evaluated when operated with green hydrogen fuel, which led to a substantial improvement in efficiency, estimated at 73.37%. However, it was found that the LCOE of the system is relatively higher and approximately 15% higher than the methane-based alternative. Cost of electricity by source ; Process engineering ; Electricity generation ; Solid oxide fuel cell ; Waste management ; Supercritical fluid ; Electricity ; Rankine cycle ; Environmental science ; Hydrogen fuel ; Natural gas ; Engineering ; Power (physics) ; Fuel cells ; Chemistry ; Electrical engineering ; Chemical engineering ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Carbon Dioxide Capture Technologies
Authors: Xiangyi Xu | Xuanxi Jiang | Meiyun Shi | Lei Yin
Source: The Analyst (review, 2023-01-01)
Field: Chemistry — Spectroscopy

Mass spectrometry-based techniques for single-cell analysis The cell is the most basic structural unit and plays a vital role in the function of an organism. Studying the heterogeneity of cells, especially the qualitative and quantitative analyses of proteins and lipids at the cellular level and even at the subcellular level, is of great significance for the study of some important pathological or physiological processes. Due to the small size of a single cell, low content of analytes and large interference from the biological matrix within the single cell, analytical methods at the single cell level must be highly sensitive and selective. Mass spectrometry is a powerful technology for single-cell analysis, because it has high sensitivity, high selectivity and the ability to monitor multiple chemicals at the same time. In this review, four mass spectrometry-based methods applied to single-cell analysis are introduced and discussed in detail; these are electrospray ionization mass spectrometry (ESI-MS), laser desorption ionization mass spectrometry (LDI-MS), secondary ion mass spectrometry (SIMS) and inductively coupled plasma mass spectrometry (ICP-MS). The recent advances in single-cell analysis with these mass spectrometry-based techniques are summarized. We believe that this review can provide some help and reference for single-cell analysis by mass spectrometry. Mass spectrometry ; Chemistry ; Analytical Chemistry (journal) ; Chromatography Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Proteomics Techniques and Applications
Authors: Jing Shang | Tao Tan | Shunan Feng | Qinrong Li | Renshuai Huang | Qinglong Meng
Source: Journal of Food Process Engineering (article, 2023-04-10)
Field: Chemistry — Analytical Chemistry

Quality attributes prediction and maturity discrimination of kiwifruits by hyperspectral imaging and chemometric algorithms Abstract The nondestructive detection of the quality attributes and maturity of kiwifruits by using hyperspectral imaging technique and chemometric algorithms were investigated. Two models of the partial least square regression and principal components regression were built up based on full variables. The multiple linear regression (MLR) was designed to develop the simplified detection models based on feature variables chosen by the successive projection algorithm and competitive adaptive reweighted sampling (CARS). Results showed that the optimal CARS‐MLR model was designed to determine the quality attributes with RPD above 2.2. Particularly, about SSC ( R 2 P = 0.896, RMSEP = 0.628%, RPD = 3.121), about firmness ( R 2 P = 0.871, RMSEP = 1.065 kg/cm 2 , RPD = 2.809). The partial least square discriminant analysis and simplified k nearest neighbor models were designed to discriminate the kiwifruits maturity stages with a classification accuracy of 93.3% and 98.3%. This research demonstrates that the hyperspectral imaging technology coupled with chemometric algorithms is promising for the quality assessment and maturity discrimination of kiwifruits. Practical applications The quality and maturity of kiwifruits have a direct relationship with the indicators of the SSC, firmness and color. Traditional methods for predicting the quality and maturity of kiwifruits are destructive, time‐consuming, and unusually affected by subjective factors. Hyperspectral imaging technique has the advantages of nondestructive, rapid, non‐pollution, and so on. The results demonstrated that the hyperspectral imaging technique coupled with chemometric algorithms can predict the quality and maturity of kiwifruits, which provides a theoretical basis to develop a real‐time detection system to predict the quality and maturity of fruits. Hyperspectral imaging ; Partial least squares regression ; Mathematics ; Linear discriminant analysis ; Principal component analysis ; Linear regression ; Pattern recognition (psychology) ; Artificial intelligence ; Multivariate statistics ; Maturity (psychological) ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis