jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: C. R. Rathish | P. Parthasarathy | S. Gopinath | L. Mariappan | P Ashok | U Arun Kumar
Source: Measurement Sensors (article, 2024-05-06)
Field: Materials Science — General Materials Science

Investigation of agricultural waste and ZrB2 nanoparticles with reinforcement in aluminum alloy matrix Agricultural waste has the potential to serve several purposes, such as reinforcement. In recent times, researchers have incorporated agricultural waste into various materials with the aim of enhancing their qualities. By acting as a kind of reinforcement, it has the potential to decrease the quantity of agricultural waste that needs further processing. This work enhances the area of advanced materials by examining the characteristics of Al 7175 alloy reinforced with zirconium diboride (ZrB2) nanoparticles and rice husk ash, which is a byproduct of agriculture. Understanding and improving the wear resistance of composites is crucial in several industrial applications. This study involves the incorporation of ZrB2 nanoparticles into the Al 7175 alloy at three distinct concentrations, with the addition of 2.5 % rice husk. This study examines the correlation between wear parameters and responses (Cf), including friction force (FF), wear rate (Sf), and friction coefficient. Sliding distance, load, composition, and speed are among the wear parameters. The aforementioned combinations result in a load of 25.2 N, a rotational speed of 400 rpm, a sliding distance of 71.2 m, and a composition of 5 %. Taguchi signal-to-noise ratio (SNR) analysis is used to identify optimal combinations for minimizing Cf, Sf, and FF. Confirmation tests are conducted in order to ensure that the results align with the expected optimal combinations. Data scientists may also use an Artificial Neural Network (ANN) model as a valuable tool. It utilizes input data to forecast replies. The ANN model demonstrates strong performance throughout testing, validation, and training, with an overall prediction rate of 87.873 %. The study's results indicate that reducing the load and speed, while increasing the concentration of ZrB2 nanoparticles, may effectively decrease the wear rate. In addition, the use of rice husk enhances the wear resistance of the base alloy by 28 %. The alloy Al 7175 may experience a reduction in wear rate and friction coefficients by the incorporation of 2.5 % rice husk and 5 % ZrB2 nanoparticles. Alloy ; Materials science ; Reinforcement ; Nanoparticle ; Aluminium ; Matrix (chemical analysis) ; Business ; Composite material ; Nanotechnology Material Properties and Applications ; Innovations in Concrete and Construction Materials ; Aluminum Alloys Composites Properties
Authors: Doeke R. Hekstra | Harrison K. Wang | Margaret A. Klureza | Jack B. Greisman | Kevin M. Dalton
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-07-23)
Field: Materials Science — Materials Chemistry

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments. Prior probability ; Bayesian probability ; Scaling ; Computer science ; Scale (ratio) ; Crystallography ; Algorithm ; Biological system ; Statistical physics ; Chemistry ; Artificial intelligence ; Mathematics ; Physics ; Biology ; Geometry Enzyme Structure and Function ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ana Raquel Bertão | Filipe Teixeira | Viktoriya Ivasiv | Píer Parpot | Cristina Almeida Aguiar | A. Fonseca | Manuel Bañobre‐López | Fátima Baltazar | Isabel C. Neves
Source: ACS Applied Materials & Interfaces (article, 2024-01-25)
Field: Materials Science — Biomaterials

Machine Learning-Assisted Optimization of Drug Combinations in Zeolite-Based Delivery Systems for Melanoma Therapy Two independent artificial neural network (ANN) models were used to determine the optimal drug combination of zeolite-based delivery systems (ZDS) for cancer therapy. The systems were based on the NaY zeolite using silver (Ag<sup>+</sup>) and 5-fluorouracil (5-FU) as antimicrobial and antineoplastic agents. Different ZDS samples were prepared, and their characterization indicates the successful incorporation of both pharmacologically active species without any relevant changes to the zeolite structure. Silver acts as a counterion of the negative framework, and 5-FU retains its molecular integrity. The data from the A375 cell viability assays, involving ZDS samples (solid phase), 5-FU, and Ag<sup>+</sup> aqueous solutions (liquid phase), were used to train two independent machine learning (ML) models. Both models exhibited a high level of accuracy in predicting the experimental cell viability results, allowing the development of a novel protocol for virtual cell viability assays. The findings suggest that the incorporation of both Ag and 5-FU into the zeolite structure significantly potentiates their anticancer activity when compared to that of the liquid phase. Additionally, two optimal AgY/5-FU@Y ratios were proposed to achieve the best cell viability outcomes. The ZDS also exhibited significant efficacy against <i>Escherichia coli</i> (<i>E. coli</i>) and <i>Staphylococcus aureus</i> (<i>S. aureus</i>); the predicted combination ratio is also effective against <i>S. aureus</i>, underscoring the potential of this approach as a therapeutic option for cancer-associated bacterial infections. Materials science ; Drug delivery ; Zeolite ; Drug ; Nanotechnology ; Chemical engineering ; Pharmacology ; Organic chemistry ; Medicine ; Catalysis ; Engineering Nanoparticle-Based Drug Delivery ; Spectroscopy Techniques in Biomedical and Chemical Research ; Nanoplatforms for cancer theranostics
Authors: Yury Kostyukevich | Sergey Osipenko | Liudmila Borisova | Albert Kireev
Source: Journal of Mass Spectrometry (article, 2024-05-12)
Field: Chemistry — Spectroscopy

In‐Electrospray source Hydrogen/Deuterium exchange coupled to multistage fragmentation for the investigation of the protonation and fragmentation pathways of gas phase ions Identification of molecules in complex natural matrices relies on matching the fragmentation spectra of ions under investigation and the spectra acquired for the corresponding analytical standards. Currently, there are many databases of experimentally measured tandem mass spectrometry spectra (such as NIST, MzCloud, and Metlin), and considerable progress has been made in the development of software for predicting tandem mass spectrometry fragments in silico using combinatorial, machine learning, and quantum chemistry approaches (such as MetFrag, CFM-ID, and QCxMS). However, the electrospray ionization molecules can be ionized at different sites (protonated or deprotonated), and the fragmentation spectra of such ions are different. Here, we are using the combination of the in-ESI source hydrogen/deuterium exchange reaction and MS<sup>n</sup> fragmentation for the investigation of the fragmentation pathways for different protomers of organic molecules. It is shown that the distribution of the deuterium in the fragment ions reflects the presence of different protomers. For several molecules, the distribution of deuterium was traced up to the MS<sup>5</sup> level of fragmentation revealing many unusual and unexpected effects. For example, we investigated the loss of HF from the ciprofloxacin and norfloxacin ions and observed that for ions protonated at -COOH group, the eliminating hydrogen always comes from -NH group. When ions are protonated at another site, the elimination of hydrogen with a probability of 30% occurs from the -NH group, and with a probability of 70%, it originates from other sites on the molecule. Such effects were not described previously. Quantum chemical simulation was used for the verification of the protonated structures and simulation of the corresponding fragmentation spectra. Chemistry ; Fragmentation (computing) ; Protonation ; Deuterium ; Molecule ; Tandem mass spectrometry ; Hydrogen–deuterium exchange ; Ion ; Mass spectrometry ; Electrospray ionization ; Mass spectrum ; Hydrogen ; Electrospray ; Analytical Chemistry (journal) ; Organic chemistry ; Chromatography ; Atomic physics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Oleksandr Stroyuk | Oleksandra Raievska | Jens Hauch | Christoph J. Brabec
Source: Chemical Communications (review, 2024-12-02)
Field: Materials Science — Materials Chemistry

Atomically thin 2D materials for solution-processable emerging photovoltaics Atomically thin 2D materials, such as graphene and graphene oxide, covalent organic frameworks, layered carbides, and metal dichalcogenides, reveal a unique variability of electronic and chemical properties, ensuring their prospects in various energy generation, conversion, and storage applications, including light harvesting in emerging photovoltaic (ePV) devices with organic and perovskite absorbers. Having an extremely high surface area, the 2D materials allow a broad variability of the bandgap and interband transition type, conductivity, charge carrier mobility, and work function through mild chemical modifications, external stimuli, or combination with other 2D species into van-der-Waals heterostructures. This review provides an account of the most prominent "selling points" of atomically thin 2D materials as components of ePV solar cells, including highly tunable charge extraction selectivity and work function, structure-directing and stabilizing effects on halide perovskite light absorbers, as well as broad adaptability of 2D materials to solution-based manufacturing of ePV solar cells using sustainable and upscalable printing technologies. A special focus is placed on the large potential of the materials discovery and design of ePV functionalities based on van-der-Waals stacking of atomically thin 2D building blocks, which can open a vast compositional domain of new materials navigable with machine-learning-based accelerated materials screening. Graphene ; Materials science ; Photovoltaics ; Oxide ; Nanotechnology ; Covalent bond ; Carbide ; Thin film ; Electronic materials ; Metal ; Photovoltaic system ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Ignacio Borge-Durán | Eduardo Aguilar-Bejarano | Luis Arrieta Araya | Mauricio Gutierrez Arguedas | Ender Özcan | Simon Woodward | Grazziela P. Figueredo
Source: Research Square (preprint, 2024-05-15)
Field: Materials Science — Materials Chemistry

Explainable GNN-Derived Structure-Property Relationships in Interstitial-Alloy Materials Crystal structure ; Lattice (music) ; Generality ; Interpretability ; Statistical physics ; Materials science ; Computer science ; Physics ; Chemistry ; Crystallography ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Maryam Mousavifard | Elahe Abedi | Kazem Alirezalu
Source: LWT (article, 2024-03-20)
Field: Chemistry — Analytical Chemistry

Ensemble machine learning as a mathematical approach to predict high-voltage electric field-assisted removal of color and oxidative indices from soybean oil using the activated porous electrodes Porous electrode materials provide a large surface area for electrochemical reactions and facilitate ion transport efficiently. By optimizing the design and properties of porous electrodes, it is possible to enhance the bleaching performance. In this research, three machine learning (ML) algorithms, namely Support Vector Regression, Ridge Regression, and Decision Tree Regression were utilized for predicting the bleaching efficiency under different conditions of HVEF-assisted bleaching (HVEFAB). Subsequently, the outcomes of these models were compared to ensemble models created by combining the algorithms as mentioned earlier using three techniques including bagging, boosting, and stacking. The levels of chlorophyll and carotenoids were reduced as a result of increased voltage, modification in the electrode surface, number of cathode-anode, and the presence of an inert electrolyte. Meanwhile, the reduction of red color and chlorophyll pigments was more significant than yellow color and carotenoid pigments. Moreover, the application of HVEFAB can potentially decrease the levels of polar compounds, such as oxidative compounds on the electrode surface. Based on the MSE, MAE, and R2 metrics, the algorithms' efficacy in forecasting outputs 1 (color index) and 2 (oxidative index) can be ordered as follows: Stacking > Boosting > Bagging > Decision Tree (DT) > Ridge > Support Vector Regression (SVR). The findings demonstrate that integrating these three models, mainly through the stacking technique, substantially improves the accuracy of the predicting model. Particle swarm optimization (PSO) and polynomial regression rather than RSM represented a superior model to predict red color removal. Electrode ; Electric field ; Porosity ; Oxidative phosphorylation ; Voltage ; Materials science ; Artificial intelligence ; Computer science ; Environmental science ; Chemistry ; Composite material ; Engineering ; Electrical engineering ; Physics ; Biochemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Jun Li | Junkang Sun | Yixiao Wang | Xiangfu Wang
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Prediction of metastable energy level distribution of D<sup>3+</sup> (D = Cr and Fe) doped phosphors based on machine learning This paper employs regression models based on machine learning to propose a method for predicting the energy level distribution rules of Cr 3+ and Fe 3+ in various doped crystals. Metastability ; Doping ; Materials science ; Phosphor ; Energy (signal processing) ; Distribution (mathematics) ; Energy distribution ; Computational physics ; Optoelectronics ; Atomic physics ; Statistics ; Physics ; Mathematics ; Mathematical analysis Molecular Sensors and Ion Detection ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Yaqi Liu | Ziran Liu | Tengfei Xu | Dominik Legut | Xingyao Yin | Ruifeng Zhang
Source: Computational Materials Science (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Classification enhanced machine learning model for energetic stability of binary compounds Binary number ; Stability (learning theory) ; Materials science ; Artificial intelligence ; Thermodynamics ; Computer science ; Machine learning ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Ricki Chairil | Allison P. Forsberg | Richard L. Brutchey | Noah Malmstadt
Source: Reaction Chemistry & Engineering (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

High-throughput reaction discovery for Cs–Pb–Br nanocrystal synthesis We developed a 16-channel flow reactor for high-throughput reaction screening, coupled with machine learning for product mapping and prediction. Nanocrystal ; Throughput ; Nanotechnology ; Computer science ; Combinatorial chemistry ; Materials science ; Chemistry ; Operating system Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electronic and Structural Properties of Oxides
Authors: Lijun Pan | Jesús Carrete | Zhao Wang | Georg K. H. Madsen
Source: The Journal of Physical Chemistry C (article, 2024-06-22)
Field: Materials Science — Materials Chemistry

Phonon Transport in Defect-Laden Bilayer Janus PtSTe Studied Using Neural-Network Force Fields We explore the phonon transport properties of defect-laden bilayer PtSTe using equilibrium molecular dynamics simulations based on a neural-network force field. Defects prove very efficient at depressing the thermal conductivity of the structure, and flower defects have a particularly powerful effect, comparable to that of double vacancies. Furthermore, the conductivity of the structure with flower defects exhibits an unusual temperature dependence due to structural instability at high temperatures. We look into the distortion to normal modes around the defect by means of the projected phonon density of states and find diverse phenomena including localized modes and blue shifts. Phonon ; Condensed matter physics ; Thermal conductivity ; Janus ; Bilayer ; Distortion (music) ; Molecular dynamics ; Materials science ; Instability ; Conductivity ; Chemical physics ; Nanotechnology ; Physics ; Chemistry ; Optoelectronics ; Mechanics ; Membrane ; Quantum mechanics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Fuel Cells and Related Materials
Authors: D D Zhu | Zhikuang Xin | Siming Zheng | Yangang Wang | Xiaoyu Yang
Source: Journal of Chemical Theory and Computation (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Addressing the Accuracy-Cost Trade-off in Material Property Prediction Using a Teacher-Student Strategy Deep learning has catalyzed a transformative shift in material discovery, offering a key advantage over traditional experimental and theoretical methods by significantly reducing associated costs. Models adept at predicting properties from chemical compositions alone do not require structural information. However, this cost-efficient approach compromises model precision, particularly in Chemical Composition-based Property Prediction Models (CPMs), which are notably less accurate than Structure-based Property Prediction Models (SPMs). Addressing this challenge, our study introduces a novel Teacher-Student (TS) strategy, where a pretrained SPM serves as an instructive 'teacher' to enhance the CPM's precision. This TS strategy successfully harmonizes low-cost exploration with high accuracy, achieving a significant 47.1% reduction in relative error in scenarios involving 100 data entries. We also evaluate the effectiveness of the proposed strategy by employing perovskites as a case study. This method represents a significant advancement in the exploration and identification of valuable materials, leveraging CPM's potential while overcoming its precision limitations. Computer science ; Key (lock) ; Property (philosophy) ; Transformative learning ; Reduction (mathematics) ; Identification (biology) ; Machine learning ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: He Sun | Shyam Dwaraknath | Michael E. West | Handong Ling | Kristin A. Persson | Sophia E. Hayes
Source: ChemRxiv (preprint, 2024-03-08)
Field: Chemistry — Spectroscopy

Machine learning of 27Al NMR quadrupolar tensors for crystalline structures from DFT NMR crystallography has emerged as promising technique for the determination and refinement of crystal structures. The crystal structure of compounds containing quadrupolar nuclei, such as 27Al, can be improved by directly comparing solid-state NMR measurements to DFT computations of the electric field gradient (EFG). The high computational cost of these first-principles calculations limits the applicability of this method to all but the most well-defined structures. We developed a fast, low-cost machine learning model to predict EFG parameters based on local structural motifs and elemental parameters. We computed 8081 EFG tensors using DFT and benchmarked them against 105 experimental parameters. Surprisingly, simple local geometric features dominate the predictive performance of the resulting random-forest model, yielding an R2 value of 0.98 and an RMSE of 0.61 MHz. This model accuracy should enable pre-refining future structural assignments before finally validating with first-principles calculations. Such a catalogue of 27Al NMR tensors can serve as a tool for researchers assigning complex quadrupolar spectra. Crystallography ; Nuclear magnetic resonance ; Materials science ; Physics ; Chemistry Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Superconducting Materials and Applications
Authors: Rohini Maram | Sathishkumar Samiappan | Seung-Chul Yoon
Source:  (article, 2024-07-07)
Field: Chemistry — Analytical Chemistry

Detection and Classification of Plastic Contaminants on Chicken Meat using Color and Hyperspectral Imaging This paper proposes an innovative two-step deep learning methodology for detecting and classifying various small plastic contaminants (5 mm × 5 mm and 2 mm × 2 mm) on chicken breast fillets. It combines YOLO object detection with RGB images for precise localization and 1D-CNN classification with hyperspectral images (600-1,700 nm) for accurate material identification, utilizing 12 common polymer types like HDPE, LDPE, PET, PVC, and PP. The study employed 52 color and 52 hyperspectral images. Both YOLOv7x and YOLOv8x excelled at detecting 5 mm plastic foreign materials (FMs) with a detection accuracy of 99 %. However, YOLOv7x outperformed YOLOv8x by 15 % for 2 mm FMs (80% vs. 65%). The classification accuracy of the 1D-CNN model using hyperspectral images was 97 % in classifying 12 common plastic contaminant types on chicken meat surfaces. This study demonstrates color imaging facilitating real-time detection of foreign materials and hyperspectral imaging enabling precise material identification. The study results pave the way for efficient and automated quality control and enhancement of safety standards in poultry processing. Hyperspectral imaging ; Contamination ; Environmental science ; Artificial intelligence ; Remote sensing ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Geology ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food