jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryama Hammi
Source: Materials for Renewable and Sustainable Energy (article, 2023-09-02)
Field: Materials Science — Ceramics and Composites

Exploratory study of potential usefulness of cost-effective composites thin films for energy device Abstract In this study, we synthesized neat and loaded lead phosphate glass (PbO–P 2 O 5 ) with the inclusion of Cr, Co, Ni, and Zn using an inexpensive sol–gel technique. These composites were then deposited on silica glass substrates. Our objective was to investigate the influence of these fillers on the properties of the glass. The concentrations of the fillers were varied from 0 to 16 wt%, and the resulting thin films were characterized by measuring the absorption coefficient and estimating the optical band gap at room temperature. Additionally, we measured the electrical resistivity of the semiconducting thin films as a function of filler concentrations and temperature. To assess the overall performance of the films, we calculated the figure of merit using the Iles and Soclof approach, considering the DC resistance versus free carrier concentration and absorption coefficient. Interestingly, our results revealed a significant improvement in the figure of merit at specific filler concentrations. The obtained results are comprehensive and provide detailed insights. They indicate that the thin films produced in this study have the potential to be useful in energy devices, particularly in applications involving P–N junctions and similar structures. Figure of merit ; Materials science ; Composite material ; Thin film ; Attenuation coefficient ; Temperature coefficient ; Electrical resistivity and conductivity ; Filler (materials) ; Band gap ; Absorption (acoustics) ; Optics ; Optoelectronics ; Nanotechnology Glass properties and applications ; Luminescence Properties of Advanced Materials ; Transition Metal Oxide Nanomaterials
Authors: Roya Boodaghi Malidarre | İskender Akkurt | Hesham M.H. Zakaly | Abdul Vahap Korkmaz | İsmail Ekmekçi
Source: Research Square (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Harnessing Fe 2 O 3 to Improve HAP Composites: Investigating Radiation Shielding, Mechanical Attributes, and Magnetic Field Effects Abstract Hydroxyapatite (HAP) bio-composites are widely utilized for the repair and replacement of bone and teeth tissues in humans. However, due to their suboptimal mechanical properties, the strength and durability of pure HAP have been improved by incorporating various alloys and materials. This study examines the radiation shielding and mechanical properties of Fe 2 O 3 -reinforced HAP composites for use as implants, with Fe 2 O 3 concentrations of 0.0, 2.5, 5.0, and 7.5 wt.%. The study also investigates the composite's behavior in a magnetic field. Using the FLUKA Monte Carlo Code, radiation shielding factors are determined, and the mechanical properties of the composites are assessed through theoretical methods. Results indicate that increasing Fe 2 O 3 content in HAP bio-composites enhances the radiation shielding and mechanical properties of the selected samples. Additionally, without a magnetic field, particle distribution displays symmetry along the x-axis; however, when exposed to a magnetic field of Bx=5 microT, a distinct pattern emerges. Electromagnetic shielding ; Materials science ; Composite material ; Composite number ; Radiation shielding ; Durability ; Radiation ; Magnetic field ; Optics Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Radioactivity and Radon Measurements
Authors: K. D. Bopanna | C.S.L. Vijaya Durga | Harjeet Singh | Nisha Gupta | Ahmed Fouad | Ginni Nijhawan
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Materials Science — Ceramics and Composites

RETRACTED: Enhanced Sintering Performance of Ceramic Composites Fabricated by Powder Metallurgy The Publisher has been made aware of ethical breaches affecting this proceeding published in E3S Web of Conferences, Volume 430 (2023) . These instances involve a specific author, K.K. Saxena who used citation manipulation and inappropriate references in 47 articles, for a total of 310 citations. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. See our publishing ethics policies . The Guest Editor of the proceedings volume endorsed the Publisher's decision to retract these articles. Web of Conferences is extremely grateful to the whistleblower for bringing this case to our attention. See the retraction notice E3S Web of Conferences 430 , 00002 (2023), https://doi.org/10.1051/e3sconf/202443000002 Materials science ; Sintering ; Microstructure ; Ceramic ; Powder metallurgy ; Composite material ; Porosity ; Cubic zirconia ; Fracture toughness ; Ball mill ; Metallurgy Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Advanced materials and composites
Authors: Naim Abdul-Khalek | Reinhard Wimmer | Michael T. Overgaard | Simon Gregersen Echers
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-02-13)
Field: Chemistry — Spectroscopy

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach Abstract Accurate and absolute quantification of individual peptides in complex mixtures is a challenge not easily overcome. A potential solution is the use of quantitative mass spectrometry (MS) based methods, however, current state of the art requires foreground knowledge and isotopically labeled standards for each peptide to be accurately quantified. This increases analytical expenses, time consumption, and labor, limiting the number of peptides that can be quantified. A key step in developing less restrictive label-free quantitative peptidomics methods is understanding of the physicochemical properties of peptides that influence the MS response. In this work, a deep learning model was developed to identify the most relevant physicochemical properties based on repository MS data from equimolar peptide pools. Using an autoencoder with attention mechanism and correlating attention weights with corresponding physicochemical property indices from AAindex1, we were able to obtain insight on the properties governing the peptide-level MS1 response. These properties can be grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, we present a model for predicting the MS1 intensity output based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 11%. Peptide ; Mass spectrometry ; Chemistry ; Limiting ; Biological system ; Deep learning ; Quantitative proteomics ; Proteomics ; Sequence (biology) ; Autoencoder ; Computer science ; Chromatography ; Computational biology ; Artificial intelligence ; Biochemistry ; Biology ; Engineering Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Tingyu Zhu | Zhiqiang Wang | Zilin Zhang | Xiuhan He | Gangao Li | Zongbao Huang | Lili Guo | Zhiwei Li | Huiling Du
Source: Plant Soil and Environment (article, 2023-12-13)
Field: Chemistry — Analytical Chemistry

Hyperspectral analysis of the content of the alkali-hydrolysed nitrogen in the soil of a millet field Hyperspectral imaging technology has emerged as a prominent research area for quantitatively estimating soil nutrient content owing to its non-destructive, rapid, and convenient features. Our work collected the data from soil samples using the hyperspectrometer. Then, the data were processed. The competitive adaptive reweighted sampling (CARS) algorithm reduced the original 148 bands to 13, which accounted for 8.8% of the total bands. These selected bands possess a certain level of interpretability. Based on the modelling results, it can be concluded that the prediction model constructed by the least squares support vector machine (LSSVM) exhibited the highest accuracy. The coefficient determination, root mean square error, and ratio performance deviation were 0.8295, 2.95, and 2.42, respectively. These findings can provide theoretical support for the application of hyperspectral technology in detecting the content of the AHN in soil. Moreover, they can also serve as a reference for the rapid detection of other soil components. Hyperspectral imaging ; Interpretability ; Content (measure theory) ; Partial least squares regression ; Soil science ; Sampling (signal processing) ; Soil nutrients ; Coefficient of determination ; Correlation coefficient ; Mean squared error ; Mathematics ; Environmental science ; Statistics ; Computer science ; Artificial intelligence ; Soil water ; Computer vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: L. Sun | Yuanbo Li | Mohan Chen
Source: arXiv (Cornell University) (preprint, 2023-04-07)
Field: Materials Science — Materials Chemistry

Truncated Non-Local Kinetic Energy Density Functionals for Simple Metals and Silicon Adopting an accurate kinetic energy density functional (KEDF) to characterize the noninteracting kinetic energy within the framework of orbital-free density functional theory (OFDFT) is challenging. We propose a new form of the non-local KEDF with a real-space truncation cutoff that satisfies the uniform electron gas limit and design KEDFs for simple metals and silicon. The new KEDFs are obtained by minimizing a residual function, which contains the differences in the total energy and charge density of several representative systems with respect to the Kohn-Sham DFT results. By systematically testing different cutoffs of the new KEDFs, we find that the cutoff plays a crucial role in determining the properties of metallic Al and semiconductor Si systems. We conclude that the new KEDF with a sufficiently long cutoff performs even better than some representative non-local KEDFs in some aspects, which sheds new light on optimizing the KEDFs in OFDFT to achieve better accuracy. Cutoff ; Kinetic energy ; Truncation (statistics) ; Density functional theory ; Semiconductor ; Silicon ; Limit (mathematics) ; Statistical physics ; Simple (philosophy) ; Function (biology) ; Energy (signal processing) ; Residual ; Physics ; Computational physics ; Mathematics ; Quantum mechanics ; Mathematical analysis ; Algorithm ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Semiconductor materials and devices
Authors: Laila Abd-Elmegid | Mohamed Hegazy | Sayed Abdelgaber | Alexey Sulavko | Alexander Samotuga | Anas Khan | Chetan Vudadha | Riya Dalal | Simrat Randhawa | Rahul Vishwakarma | Shrey Modi | Ashok Kumar | Karthika Renuka | Harshitha Phaniraj | Fatima Jabbar | Erik Kucera | Oto Haffner | Lukas Beno | Rudolf Pribis | Martin Pajpach | Dominik Janeck | Syed Naushad | Radu-Casian Mihailescu | Jan Baranovic | Adam Juri | Michal Hamrk | imon Zajac | Tanya Mendez | G Subramanya Nayak | ubo Sekerk | Juraj Ki | Rohit Rajesh | Nitin Arul | Anita Agrawal | Meghana Shrivastava | Pragati Kumar | Data Bhaskar | Rahma Aman | Mohammad Rizwan | Astitva Kumar | Nanda Kumar | Punith Bekal | Pramod Kumar | Ghanshyam Prabhu | Zuzana Zov | Vladimr Kuti | Ruslan Fedorov | Vyacheslav Sherkunov | Dmitry Generalov | Nikita Gladilin | Valeriy Sapunov | Igor Shepelev | Arav Dhoot | N Ben | M Turki-Hadj Alouane | Vinay Kumar | Vivek Rai | Gaurav Kumar | Kumar Kushwaha | Ashish Pandey | Sadanand Yadav | Vikas Bhat | Sanchit Choudhary | Anjali Singh | Riti Kushwaha | Himanshu Rawat | Suman Bando | Aman Dass | Lakshya Panwar | Divya Srivastava | Gaurav Singal | Ankur Saharia | Dr Tiwari | Kirti Kaharwar | Ram Bhagat | Jagendra Singh | Neha Garg | Sonal Kukreja | Mayank Saini | Ishaan Singh | Shachi Mall | Keerthan Nekkanti | Saisumanth Lade | Musica Supriya | Arun Kumar | Deepak Rai | Janhavi Agrawal | Deepika Pandey | Divyanshu Pandey | Mudit Jain
Source:  (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

MoSICom 2023 Table of Contents Table (database) ; Computer science ; Database Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Thermal Expansion and Ionic Conductivity
Authors: Hannah Pierson | Xi Li | Mr Zhou | Mohammad Hossain | Mr Reitano | Adnan Rakin | Rakib Uddin | Amit Dikshit | Jin Wallner | Mr | Rana Siddharth | Fu-Gow Tarntair | Pratap Jitendra | India Singh | Ray-Hua Horng | Sanghoon Jin | Montoya Csar | Ocampo | Jhon Torres | Mr Fujii | Yali Huang | Zhu Xiang | Yu Xianbin | Jiang Hao | F Muoz | V Ferrando | J Castro | J Arias-Gonzalez | Mrs | Leguay | Hoque Ariful | Sojib | Mr Rezwan | Mohammad Sayeed | Ozgur | Mia Raley | Allison Marn | Saroj Thapa | Rebecca Swertfeger | Mark Crowley | Sethia Anuj | Mr Smith | Mr Amir | Hanen Chenini | Ashutosh Singh | Mr Kuzmin | Daniel Oblak | D Andrea | Amico | Gabriele Galimberti | Curri Vittorio | Sen Dipankar | Mr Ze-Tian | Alma Fernndez | Mr Henrich | Sakiko Okumoto | Aamir Mushtaq | Ms Journigan | Mr Siebenaller | Ms Truong | Mr Mohamed | Yaseen Noor | Mr Dipendra Khatri | Ms Sanjana Bhatia | Saiful Md | Sumon Islam | Mr Crisan | Mr Shrivatch Sankar | Ms Youngjin
Source:  (article, 2023-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

IPC 2023 Full Program Effect of the forming gas annealing on the Zinc Gallium Oxide based Deep UV photodetector characteristics grown by Annealing (glass) ; Gallium ; Zinc ; Optoelectronics ; Zinc compounds ; Materials science ; Photodetector ; Computer science ; Metallurgy Ga2O3 and related materials
Authors: Tarek Iraki | Lukas Morand | Norbert Link | Stefan Sandfeld | Dirk Helm
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Accurate Distances Measures and Machine Learning of the Texture-Property Relation for Crystallographic Textures Represented by One-Point Statistics The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g., important in forming operations. In materials science, crystallographic texture is commonly described by the orientation distribution function, which is defined as the probability density function of the orientations of the monocrystal grains conforming a polycrystalline material. For representing the orientation distribution function, there are several approaches such as using generalized spherical harmonics, orientation histograms, and pole figure images . Measuring distances between crystallographic textures is essential for any task that requires assessing texture similarities, e.g. to guide forming processes. Therefore, we introduce novel distance measures based on (i) the Earth Movers Distance that takes into account local distance information encoded in histogram-based texture representations and (ii) a distance measure based on pole figure images. For this purpose, we evaluate and compare existing distance measures for selected use-cases. The present study gives insights into advantages and drawbacks of using certain texture representations and distance measures with emphasis on applications in materials design and optimal process control. Texture (cosmology) ; Histogram ; Orientation (vector space) ; Distribution function ; Anisotropy ; Probability density function ; Function (biology) ; Computer science ; Measure (data warehouse) ; Artificial intelligence ; Geometry ; Mathematics ; Statistics ; Image (mathematics) ; Data mining ; Physics ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Image Retrieval and Classification Techniques
Authors: Kara A. Zielinski | Shuo Sui | Suzette A. Pabit | Daniel Rivera | Tong Wang | Qingyue Hu | M.M. Kashipathy | Stella Lisova | Chris B. Schaffer | Valerio Mariani | Mark S. Hunter | Christopher Kupitz | Frank R. Moss | Frédéric Poitevin | Thomas D. Grant | Lois Pollack
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-05-24)
Field: Materials Science — Materials Chemistry

RNA structures and dynamics with Å resolution revealed by x-ray free electron lasers RNA macromolecules, like proteins, fold to assume shapes that are intimately connected to their broadly recognized biological functions; however, because of their high charge and dynamic nature, RNA structures are far more challenging to determine. We introduce an approach that exploits the high brilliance of x-ray free electron laser sources to reveal the formation and ready identification of Å scale features in structured and unstructured RNAs. New structural signatures of RNA secondary and tertiary structures are identified through wide angle solution scattering experiments. With millisecond time resolution, we observe an RNA fold from a dynamically varying single strand through a base paired intermediate to assume a triple helix conformation. While the backbone orchestrates the folding, the final structure is locked in by base stacking. In addition to understanding how RNA triplexes form and thereby function as dynamic signaling elements, this new method can vastly increase the rate of structure determination for these biologically essential, but mostly uncharacterized macromolecules. RNA ; Stacking ; Macromolecule ; Folding (DSP implementation) ; Base pair ; Nucleic acid structure ; DNA ; Biophysics ; Millisecond ; Function (biology) ; Helix (gastropod) ; Resolution (logic) ; Chemistry ; Computational biology ; Physics ; Biology ; Computer science ; Cell biology ; Biochemistry ; Gene ; Quantum mechanics Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Advanced Electron Microscopy Techniques and Applications
Authors: T. Oestereich | Ralf Tonner | Julia Westermayr
Source: arXiv (Cornell University) (preprint, 2023-09-15)
Field: Materials Science — Materials Chemistry

Decoding Energy Decomposition Analysis: Machine-Learned Insights on the Impact of the Density Functional on the Bonding Analysis The concept of chemical bonding is a crucial aspect of chemistry that aids in understanding the complexity and reactivity of molecules and materials. However, the interpretation of chemical bonds can be hindered by the choice of the theoretical approach and the specific method utilized. This study aims to investigate the effect of choosing different density functionals on the interpretation of bonding achieved through energy decomposition analysis (EDA). To achieve this goal, a data set was created, representing four bonding groups and various combinations of functionals and dispersion correction schemes. The calculations showed significant variation among the different functionals for the EDA terms, with the dispersion correction terms exhibiting the highest variability. More information was extracted by using unsupervised learning in combination with dimensionality reduction on the data set. Results indicate that, despite the differences in the EDA terms obtained from different functionals, the functional has the least significant impact, suggesting minimal influence on the bonding interpretation. Interpretation (philosophy) ; Decomposition ; Set (abstract data type) ; Dispersion (optics) ; Density functional theory ; Computer science ; Dimensionality reduction ; Chemical bond ; Energy (signal processing) ; Curse of dimensionality ; Mathematics ; Materials science ; Algorithm ; Chemistry ; Computational chemistry ; Artificial intelligence ; Physics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Fabrina Oliveira Paranhos
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Networks for Assessing Spirulina Adulteration through Digital Images A Arthrospira (Spirulina) platensis é um alimento que vem ganhando espaço na indústria de suplementos por seu alto valor nutritivo. É a cianobactéria mais utilizada no consumo humano e animal e contém alto teor de proteínas e aminoácidos e quantidades significativas de betacaroteno, vitaminas do complexo B, ferro, minerais essenciais e ácido gama-linolênico. Dada sua importância, é um alimento com alto valor de mercado e que pode ser alvo de adulterações, comprometendo a segurança alimentar de seus consumidores. Assim detectá-las se faz de extrema importância e necessidade. Este estudo objetivou avaliar de forma controlada, adulteração de Spirulina com bicarbonato de sódio, em percentuais de 10%, 15% e 25% m/m. A escolha do adulterante se deu pelo fato do mesmo participar do processo de cultivo desta microalga, por ser de baixo custo e não higroscópico. Um sistema de captação de imagens digitais foi montado, testes de otimização do mesmo foram realizados. Realizou-se a captação de imagens digitais das amostras a 0%, 10%, 15% e 25% m/m. Os dados fornecidos pelas imagens digitais nos canais RGB foram analisados através do software ChemoStat e os gráficos de análise de componentes principais (PCA) e análise de agrupamento hierárquico (HCA) foram plotados. Estes métodos quimiométricos de análise exploratória mostraram-se eficientes quando os estudos foram realizados com poucas amostras, contudo quando do aumento destas (120 amostras com captações realizadas em triplicata, totalizando 360 captações), os gráficos não as classificaram bem, houve aglomeração de dados, dificultando a análise dos mesmos. Neste momento, a Inteligência Artificial, mais especificamente as Redes Neurais Convolucionais, foi utilizada. Os algoritmos de classificação utilizados, ResNet-18 e ResNet-50, apresentaram resultados satisfatórios na classificação das amostras. Ambos tiveram uma média de 98,9% de precisão na validação e 88,5% e 98%, respectivamente, na média da precisão dos testes. Desse modo, as Redes Neurais Convolucionais associadas às imagens digitais foram muito eficientes na classificação das amostras adulteradas frente ao uso de PCA e HCA, podendo ser uma aliada com grande potencial de crescimento e diversificação em análises, amplamente utilizadas nas diversas áreas dentro da Química. Spirulina (dietary supplement) ; Cyanobacteria Spectroscopy and Chemometric Analyses ; Botanical Research and Applications ; Phytochemical compounds biological activities