jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Asuka Ochi | S. Asakura | Manami Goto | Shunsuke Yagi | Ikuya Yamada | Hidekazu Ikeno
Source: Journal of the Ceramic Society of Japan (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Composition dependent synergistic effects on oxygen evolution reaction catalysis for CaFe&lt;sub&gt;1−&lt;/sub&gt;&lt;i&gt;&lt;sub&gt;x&lt;/sub&gt;&lt;/i&gt;Mn&lt;i&gt;&lt;sub&gt;x&lt;/sub&gt;&lt;/i&gt;O&lt;sub&gt;3&lt;/sub&gt; We investigate oxygen evolution reaction (OER) catalytic activity for CaFe1−xMnxO3. The OER activity of CaFe1−xMnxO3 decreases almost monotonically with increasing Mn content x. No any compositions do not exceeded CaFeO3 in OER activity, but we observe that the OER activity is gently decreased except for Fe-rich side (x = 0.1) in comparison with the weighted average OER activity between the endmembers of highly active CaFeO3 (x = 0) and less active CaMnO3 (x = 1). We discuss probable electronic effects of the Fe4+–Mn4+ couple on OER activity based on the density functional theory calculation using the special quasirandom structure models. The local electronic states of Mn ions were deactivated by Fe substitution on the Fe-rich side (x = 0.25), whereas the Fe ions are constantly activated by Mn substitution in the entire composition range. This finding proposes that local electronic states are variously changed by the kind of couples of multiple elements, which may contribute to the design of mixed transition metal oxides for OER catalysts. Catalysis ; Oxygen evolution ; Oxygen ; Materials science ; Composition (language) ; Manganese ; Physical chemistry ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Magnetic and transport properties of perovskites and related materials
Authors: Ke Xu | Ting Liang | Nan Xu | Penghua Ying | Shunda Chen | Ning Wei | Jianbin Xu | Zheyong Fan
Source: arXiv (Cornell University) (preprint, 2024-11-14)
Field: Materials Science — Materials Chemistry

NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties This repository provides the source code, datasets, training/testing processes, and demos for the NEP-MB-pol Water Model, as supplementary material to Xu et al., NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties [https://arxiv.org/abs/2411.09631]. Computer science Machine Learning in Materials Science
Authors: Namrata Ramesh | Hrishit Banerjee | Jack E. N. Swallow | Erik Björklund | Ava Dean | Pravin N. Didwal | Michael Fraser | Conor Phelan | Lijin An | Jasper Singh | Jarrod Lewis | Weixin Song | Robert A. House | Andrew Morris | Robert S. Weatherup | Rebecca J. Nicholls
Source: ChemRxiv (preprint, 2024-07-01)
Field: Materials Science — Surfaces, Coatings and Films

An atomistic interpretation of the oxygen K-edge X-ray absorption spectra of layered Li-ion battery cathode materials Core loss spectroscopies\ncan provide powerful element-specific\ninsight into the redox processes occurring in Li-ion battery cathodes,\nbut this requires an accurate interpretation of the spectral features.\nHere, we systematically interpret oxygen K-edge core loss spectra\nof layered lithium transition-metal (TM) oxides (LiMO&lt;sub&gt;2&lt;/sub&gt;,\nwhere M = Co, Ni, Mn) from first principles using density-functional\ntheory (DFT). Spectra are simulated using three exchange–correlation\nfunctionals, comprising the generalized gradient approximation (GGA)\nfunctional PBE, the DFT–PBE + Hubbard &lt;i&gt;U&lt;/i&gt; method,\nand the &lt;i&gt;meta&lt;/i&gt;-GGA functional rSCAN. In general, rSCAN\nprovides a better match to experimentally observed excitation energies\nof spectral features compared to both PBE and PBE + &lt;i&gt;U&lt;/i&gt;, especially at energies close to the main edge. Projected density\nof states of core-hole calculations show that the O orbitals are better\ndescribed by rSCAN. Hybridization, structural distortions, chemical\ncomposition, and magnetism significantly influence the spectra. The\nO K-edge spectrum of LiNiO&lt;sub&gt;2&lt;/sub&gt; obtained using rSCAN shows\na closer match to the experimental X-ray absorption spectroscopy (XAS)\nwhen derived from a simulation cell which includes a Jahn–Teller\ndistortion, showing that the DFT-calculated pre-edge feature contains\ninformation about not only chemical species but also geometric distortion.\nCore loss spectra derived from DFT can also differentiate between\nmaterials with the same structure and magnetic configuration but comprising\ndifferent TMs; these differences are comparable to those observed\nin experimental XAS from the same materials. This foundational work\nhelps establish the extent to which DFT can be used to bridge the\ninterpretation gap between experimental spectroscopic signatures and\nab initio methods describing complex battery materials, such as lithium\nnickel manganese cobalt oxides. Cathode ; Interpretation (philosophy) ; Ion ; Oxygen ; Materials science ; Battery (electricity) ; Spectral line ; Enhanced Data Rates for GSM Evolution ; Absorption (acoustics) ; Atomic physics ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Physical chemistry ; Optics ; Computer science ; Telecommunications ; Composite material ; Thermodynamics ; Environmental chemistry Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials ; X-ray Spectroscopy and Fluorescence Analysis
Authors: S. Sani | Aminu Aliyu | Ahmad Ibrahi̇m Bagudo | Adamu Almustapha Aliero | A A Umar | Mujtaba Haruna | M. Usman
Source: UMYU Scientifica (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Review on Bacteria Associated with Metal Rusting Metal rusting, also known as corrosion, is the deterioration of a material's characteristics, particularly metals, caused by chemical or electrochemical reactions in the surrounding environment. It consists of the interaction of iron or steel with atmospheric oxygen and moisture, resulting in the creation of iron oxide (rust). Bacteria have an important impact on the development and advancement of metal corrosion. microbiologically influenced corrosion (MIC), is becoming increasingly problematic as it affects multiple materials and industries in society. MIC demonstrates the possible negative effect that microorganisms may cause to a substance. Different categories of bacteria, such as sulfate reducing, sulfate oxidizing, slime forming, and iron oxidizing, are active bacteria involved in bio-corrosion. The bacteria have evolved different ways to survive in the metal-polluted surroundings, including an efflux system pump, complexation/stabilization, enzymatic transformation/detoxification, and plasmid mediation. Effective management of microbial corrosion in different industrial and environmental settings requires the integration of microbiology, materials science, and corrosion engineering. This paper highlights the crucial role of microbiologically driven corrosion, which leads to the deterioration of different materials and consequential economic losses. Furthermore, it highlights future studies that aim to gain a thorough understanding of the mechanisms behind bacterial-induced corrosion and develop strategies to prevent and control rusting. Corrosion ; Bacteria ; Microorganism ; Sulfate-reducing bacteria ; Metal ; Iron bacteria ; Sulfate Corrosion Behavior and Inhibition ; Microbial Applications in Construction Materials ; Building materials and conservation
Authors: Bernadett Bana | E. Zsédely
Source: Acta Agronomica Óváriensis (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Az elektronikus orr fejlődése és alkalmazása: I. Az elektronikus orr története és típusai Az aromák olyan illékony vegyületek, amelyeket az emberi szaglás érzékel, és különböző minőségi értékekkel jellemezhetők. Az aromák sokféle vegyületből állnak, és ezeknek a vegyületeknek a kombinációja határozza meg az aroma egyedi tulajdonságait. A szaglás biológiai folyamat, mely során az orr receptorai érzékelik az illékony anyagokat, majd az agy feldolgozza ezeket az információkat. Bár a szaglásnak vannak sajátos korlátai, mint például a szubjektivitás és a reprodukálhatóság nehézségei, az elektronikus-orr rendszerek fejlesztése fontos az aromák elemzésében és az illatok azonosításában. Az elektronikus-orr különböző típusai különböző érzékelő anyagok reakcióján alapulnak a kémiai gőzökkel. A legelterjedtebb típusok közé tartoznak a fém-oxid félvezető (MOS) érzékelők, a vezető polimerek (CP) és az akusztikus hullám érzékelők. Az elektronikus orr fejlődésével együtt járó adatelemzési módszereknek kulcsfontosságú szerepük van az illatok és szagok érzékelésében és azok értelmezésében. Az elektronikus orr-ok adatelemzésének fejlődése jelentős előrelépést jelent az illatok és szagok értelmezésében, és hozzájárul széles körű alkalmazásához az iparban és a kutatásokban. Physics Photochromic and Fluorescence Chemistry
Authors: Trevor A. Norton | Debswapna Bhattacharya
Source: arXiv (Cornell University) (preprint, 2024-05-31)
Field: Chemistry — Spectroscopy

Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules Diffusion probabilistic models have made their way into a number of high-profile applications since their inception. In particular, there has been a wave of research into using diffusion models in the prediction and design of biomolecular structures and sequences. Their growing ubiquity makes it imperative for researchers in these fields to understand them. This paper serves as a general overview for the theory behind these models and the current state of research. We first introduce diffusion models and discuss common motifs used when applying them to biomolecules. We then present the significant outcomes achieved through the application of these models in generative and predictive tasks. This survey aims to provide readers with a comprehensive understanding of the increasingly critical role of diffusion models. Probabilistic logic ; Biomolecule ; Diffusion ; Noise (video) ; Statistical physics ; Computer science ; Nanotechnology ; Physics ; Artificial intelligence ; Materials science Analytical Chemistry and Chromatography
Authors: Ye Ji | Arnd Koeppe | Patrick Altschuh | Lars Griem | D. Rajagopal | Britta Nestler
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A U-Net-based self-stitching method for generating periodic grain structures Work (physics) ; Field (mathematics) ; Process (computing) ; Deformation (meteorology) ; Sequence (biology) Quasicrystal Structures and Properties ; Photonic Crystals and Applications ; Metamaterials and Metasurfaces Applications
Authors: Yihong Ma | Xiaobao Huang | Bozhao Nan | Nuno Moniz | Xiangliang Zhang | Olaf Wiest | Nitesh V. Chawla
Source: arXiv (Cornell University) (preprint, 2024-02-06)
Field: Chemistry — Analytical Chemistry

Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction. Accurate yield prediction can guide chemists toward selecting high-yield reactions during synthesis planning, offering valuable insights before dedicating time and resources to wet lab experiments. While recent advancements in yield prediction have led to overall performance improvement across the entire yield range, an open challenge remains in enhancing predictions for high-yield reactions, which are of greater concern to chemists. In this paper, we argue that the performance gap in high-yield predictions results from the imbalanced distribution of real-world data skewed towards low-yield reactions, often due to unreacted starting materials and inherent ambiguities in the reaction processes. Despite this data imbalance, existing yield prediction methods continue to treat different yield ranges equally, assuming a balanced training distribution. Through extensive experiments on three real-world yield prediction datasets, we emphasize the urgent need to reframe reaction yield prediction as an imbalanced regression problem. Finally, we demonstrate that incorporating simple cost-sensitive re-weighting methods can significantly enhance the performance of yield prediction models on underrepresented high-yield regions. Perspective (graphical) ; Yield (engineering) ; Regression ; Regression analysis ; Econometrics ; Psychology ; Economics ; Computer science ; Machine learning ; Artificial intelligence ; Statistics ; Mathematics ; Thermodynamics ; Physics Spectroscopy and Chemometric Analyses
Authors: David Helminiak | Tobias Boskamp | Dong Hye Ye
Source: Electronic Imaging (article, 2024-01-19)
Field: Chemistry — Spectroscopy

Multimodal Deep Learning Approach for Dynamic Sampling with Automatic Feature Selection in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Acquisitions of mass-per-charge (m/z) spectrometry data from tissue samples, at high spatial resolutions, using Mass Spectrometry Imaging (MSI), require hours to days of time. The Deep Learning Approach for Dynamic Sampling (DLADS) and Supervised Learning Approach for Dynamic Sampling with Least-Squares (SLADS-LS) algorithms follow compressed sensing principles to minimize the number of physical measurements performed, generating low-error reconstructions from spatially sparse data. Measurement locations are actively determined during scanning, according to which are estimated, by a machine learning model, to provide the most relevant information to an intended reconstruction process. Preliminary results for DLADS and SLADS-LS simulations with Matrix-Assisted Laser Desorption/Ionization (MALDI) MSI match prior 70% throughput improvements, achieved in nanoscale Desorption Electro-Spray Ionization (nano-DESI) MSI. A new multimodal DLADS variant incorporates optical imaging for a 5% improvement to final reconstruction quality, with DLADS holding a 4% advantage over SLADS-LS regression performance. Further, a Forward Feature Selection (FFS) algorithm replaces expert-based determination of m/z channels targeted during scans, with negligible impact to location selection and reconstruction quality. Mass spectrometry imaging ; Mass spectrometry ; Sampling (signal processing) ; Desorption ; Computer science ; Feature selection ; Artificial intelligence ; Feature (linguistics) ; Matrix (chemical analysis) ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Chemistry ; Materials science ; Chromatography ; Computer vision Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Jiahao Su | Hailong Zhang | Chunguang Wang | Bo Tan | Fuchao Tian
Source: Research Square (preprint, 2024-03-27)
Field: Chemistry — Spectroscopy

A Novel Environmental pressure Compensation Approach of IR Gas Sensors in Industrial environment Abstract In order to improve the analytical accuracy and applicability of infrared gas sensors, this paper examines the need to integrate a pressure compensation algorithm into the infrared gas analyzer, identifying appropriate compensation methods and optimizing test data accuracy. First, a test platform was constructed to evaluate the accuracy of three types of infrared sensors for trace gases in industrial environments. The sensors were tested in the following order: CO 2 at concentrations of 0.5%-4.5%; CH 4 at concentrations of 2%-18%; and CO at 100-900ppm standard gas concentrations, found that the maximum value of the test error were 48%, 44.5%, 45%, respectively, do not meet the error range specified in the standard. Secondly, the constant-pressure accuracy test was carried out by selecting the same concentration of CO 2 , CH 4 and CO standard gases and substituting them into the optimized least squares compensation model, and it was found that the maximum errors of the three gas tests were 16%, 9% and 24%, which were superior to the standard error ranges. Thirdly, the pressure compensation was verified for the three infrared gas analyzers after algorithmic compensation within the pressure compensation device. Based on this, the optimized least squares-wavelet transform soft threshold coupling model were established to integrate the ambient pressure, the test concentration and the standard gas concentration with the optimization model, and it was found that the maximum measurement errors after optimization with the optimized least squares-wavelet transform soft thresholding coupling method were 0.2%, 0.05% and 1%. In summary, this paper develops an error optimization model for various environmental pressure conditions, and provides a theoretical framework for the research and development of infrared gas analyzers and other spectroscopic gas detecting instruments. Additionally, it establishes a technical foundation for constructing in-situ on-line monitoring systems for trace gases in industrial environments. Compensation (psychology) ; Environmental science ; Business ; Psychology Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Xiaoyang Hou | Tian Zhu | Milong Ren | Bo Duan | Chunming Zhang | Dongbo Bu | Shiwei Sun
Source: Bioinformatics (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

GTAM: A Molecular Pretraining Model with Geometric Triangle Awareness The test code and data of GTAM are available online at https://github.com/StellaHxy/GTAM. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: I.I. Ajibade Ajibade | L. Sa’adu
Source: International Journal of Science for Global Sustainability (article, 2024-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unlocking High Energy and Power Densities in Supercapacitors: The Role of Moisture Content in Coconut Shell Pulverized Activated Carbon Electrodes This study explores the potential of coconut shells as a sustainable source for supercapacitor electrodes. Electrochemical characterization revealed pseudocapacitive behavior with a specific capacitance of 15.57 F/g. Interestingly, samples derived from coconut shells with higher moisture content displayed superior energy and power densities, reaching 1.38 Wh/kg and 498,312 W/kg, respectively, as measured by cyclic voltammetry. These findings highlight the crucial role of moisture in enhancing the electrochemical performance of coconut shell electrodes. The utilization of coconut shells from humid regions presents a promising strategy for developing high-performance supercapacitors that offer both excellent energy storage and extended lifespan. Supercapacitor ; Shell (structure) ; Activated carbon ; Water content ; Carbon fibers ; Electrode ; Materials science ; Moisture ; Power (physics) ; Pulp and paper industry ; Environmental science ; Composite material ; Chemistry ; Capacitance ; Physics ; Engineering ; Organic chemistry Supercapacitor Materials and Fabrication ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Salah Belaıdı | Aicha Kerassa
Source: Research Square (preprint, 2024-01-26)
Field: Chemistry — Organic Chemistry

Study by computational methods of the electronic structure of a series of organic molecules and their charge transfer complexes Series (stratigraphy) ; Charge (physics) ; Molecule ; Transfer (computing) ; Organic molecules ; Computational chemistry ; Chemical physics ; Chemistry ; Materials science ; Computer science ; Physics ; Organic chemistry ; Quantum mechanics ; Parallel computing ; Geology Free Radicals and Antioxidants ; Photochemistry and Electron Transfer Studies ; Nonlinear Optical Materials Research
Authors: Yuan-Tsung Chen | Chao Huang | Pang-Yu Liu | Hung‐Wei Yen | Ran Niu | Patrick A. Burr | Katie L. Moore | Emilio Martínez‐Pañeda | Andrej Atrens | Julie M. Cairney
Source: arXiv (Cornell University) (review, 2024-04-11)
Field: Materials Science — Metals and Alloys

Hydrogen Trapping and Embrittlement in Metals -- A Review Hydrogen embrittlement in metals (HE) is a serious challenge for the use of high strength materials in engineering practice and a major barrier to the use of hydrogen for global decarbonization. Here we describe the factors and variables that determine HE susceptibility and provide an overview of the latest understanding of HE mechanisms. We discuss hydrogen uptake and how it can be managed. We summarize hydrogen trapping and the techniques used for its characterization. We also review literature that argues that hydrogen trapping can be used to decrease HE susceptibility. We discuss the future research that is required to advance the understanding of HE and hydrogen trapping and to develop HE-resistant alloys. Hydrogen embrittlement ; Hydrogen ; Trapping ; Embrittlement ; Materials science ; Nanotechnology ; Metallurgy ; Chemistry ; Biology Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Muhammad Aasim Rafique
Source: Sensors (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Exploiting Temporal Features in Calculating Automated Morphological Properties of Spiky Nanoparticles Using Deep Learning Object segmentation in images is typically spatial and focuses on the spatial coherence of pixels. Nanoparticles in electron microscopy images are also segmented frame by frame, with subsequent morphological analysis. However, morphological analysis is inherently sequential, and a temporal regularity is evident in the process. In this study, we extend the spatially focused morphological analysis by incorporating a fusion of hard and soft inductive bias from sequential machine learning techniques to account for temporal relationships. Previously, spiky Au nanoparticles (Au-SNPs) in electron microscopy images were analyzed, and their morphological properties were automatically generated using a hourglass convolutional neural network architecture. In this study, recurrent layers are integrated to capture the natural, sequential growth of the particles. The network is trained with a spike-focused loss function. Continuous segmentation of the images explores the regressive relationships among natural growth features, generating morphological statistics of the nanoparticles. This study comprehensively evaluates the proposed approach by comparing the results of segmentation and morphological properties analysis, demonstrating its superiority over earlier methods. Artificial intelligence ; Segmentation ; Pattern recognition (psychology) ; Computer science ; Convolutional neural network ; Deep learning ; Frame (networking) ; Biological system ; Computer vision Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Cell Image Analysis Techniques