jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: German Arsenovich Arutyunov | Sergey Avdoshin
Source: Proceedings of the Institute for System Programming of RAS (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

GraphTyper: Neural Types Inference from Code Represented as Graph Although software development is mostly a creative process, there are many scrutiny tasks. As in other industries, there is a trend for automation of routine work. In many cases, machine learning and neural networks have become a useful assistant in that matter. Programming is not an exception: GitHub has stated that Copilot is already used to write up to 30% of code in the company. Copilot is based on Codex, a Transformer model trained on code as a sequence. However, a sequence is not a perfect representation for programming languages. In this work, we claim and demonstrate that by combining the advantages of Transformers and graph representations of code, it is possible to achieve excellent results even with comparably small models. Inference ; Computer science ; Graph ; Code (set theory) ; Theoretical computer science ; Programming language ; Artificial intelligence Machine Learning in Materials Science ; Neural Networks and Applications ; Software Engineering Research
Authors: Danny Guaña | Qiang Liu | Michael Jeschke | D. R. M. Walton
Source:  (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

AC Breakdown Voltages of PEEK Insulation Used in Subsea Cable Connections Subsea cable connectors are crucial components in offshore power delivery systems, commonly using a solid-liquid composite insulation system. Unfortunately, limited literature exists on the electrical characterisation of the insulation material polyether ether ketone (PEEK), despite extensive research on liquid and solid insulation materials in high voltage applications. This paper investigates the AC breakdown strength of PEEK immersed in a mineral oil at 20 °C. The results are statistically analysed using both 2-parameter and 3-parameter Weibull distributions, resulting in characteristic breakdown strenghts of 30.79 kV/mm and 30.40 kV/mm, respectively. Additionally, the location of each breakdown event was identified using a self-developed image processing procedure. Subsea ; Peek ; Electrical treeing ; Materials science ; Voltage ; Electrical engineering ; Composite material ; Partial discharge ; Engineering ; Geotechnical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Karl Kristian Krull | Arlene Kühn | Julia Höhn | Titus J. Brinker | Jeroen Krijgsveld
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-07)
Field: Chemistry — Spectroscopy

Deep learning-based proteomics enables accurate classification of bulk and single-cell samples Abstract Proteins are the main drivers of cell function and disease, making their analysis a powerful technique to characterize determinants of cell identity and to identify biomarkers. Current proteomic technology has the breadth to profile thousands of proteins and even the sensitivity to access single cells, however limitations in throughput restrict its application, e.g. not allowing classification of samples according to biological or clinical status in large sample cohorts. Therefore, we developed a deep learning-based approach for the analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we designed an architecture referred to as Proformer, and show that it is superior to convolutional neural network-driven architectures, is explainable, and demonstrates robustness towards batch-effects. Based on its tabular approach, we highlight the integration of all four dimensions of proteomic measurements (retention time, mass-to-charge, intensity and ion mobility), and demonstrate enhanced sample discrimination involving a treatment with IFN-γ, despite its subtle effect on the cell’s proteome. In addition, the Proformer is not restricted to proteomic depth, and can classify cells by cell type and their differentiation status even using single-cell proteomic data. Collectively, this work presents a novel deep learning-based model for rapid classification of proteomic data, with important future implications to enhance patient stratification, early detection and single-cell analysis. Proteomics ; Computer science ; Convolutional neural network ; Proteome ; Robustness (evolution) ; Artificial intelligence ; Deep learning ; Computational biology ; Sample (material) ; Machine learning ; Pattern recognition (psychology) ; Bioinformatics ; Biology ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Cell Image Analysis Techniques
Authors: Guillaume Le Cras
Source: theses.fr (ABES) (article, 2024-12-09)
Field: Materials Science — Biomaterials

Impact on the barrier properties of multilayer biosourced materials. Implementation-morphology-properties relationships L'objectif du projet de thèse est de développer des films multicouches à base de polymères biosourcés par coextrusion multicouche dans le but d'améliorer simultanément les propriétés barrières aux petites molécules et les propriétés mécaniques. Le polyamide 11 (PA11) et l'acide polylactique (PLA) ont des propriétés complémentaires. Le PA11, par ses températures de fusion et de cristallisation plus élevées, est le polymère confineur du PLA, ce qui induit un effet de confinement aux films multicouches. L’étude se concentre ainsi sur l'élaboration de films multicouches à partir de plusieurs grades de PLA afin de mieux comprendre l'effet de la structure cristalline du PLA sur les performances des films. Un post-traitement par recuit et étirage est appliqué pour augmenter le degré de cristallinité et modifier l'orientation cristalline du PLA. Le projet de thèse explore également la faisabilité d’un système multicouche PLA/PLA afin de déterminer l’effet du confinement dans un système monomatière. Enfin, l'impact de l'incorporation d'additifs naturels dans les films multicouches de PA11/PLA est étudié afin d’améliorer les propriétés fonctionnelles et d’apporter des caractéristiques spécifiques telles que des propriétés antibactériennes ou antioxydantes. X-ray crystallography ; Pharmaceutical technology ; Semiconductor materials ; Agrégation ; Polyamide biodegradable polymer synthesis and properties ; Polymer Foaming and Composites ; Microplastics and Plastic Pollution
Authors: Zakariya El‐Machachi | Damyan Frantzov | A. Nijamudheen | Tigany Zarrouk | Miguel A. Caro | Volker L. Deringer
Source: arXiv (Cornell University) (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide Graphene oxide (GO) materials are widely studied, and yet their atomic-scale structures remain to be fully understood. Here we show that the chemical and configurational space of GO can be rapidly explored by advanced machine-learning methods, combining on-the-fly acceleration for first-principles molecular dynamics with message-passing neural-network potentials. The first step allows for the rapid sampling of chemical structures with very little prior knowledge required; the second step affords state-of-the-art accuracy and predictive power. We apply the method to the thermal reduction of GO, which we describe in a realistic (ten-nanometre scale) structural model. Our simulations are consistent with recent experimental findings and help to rationalise them in atomistic and mechanistic detail. More generally, our work provides a platform for routine, accurate, and predictive simulations of diverse carbonaceous materials. Graphene ; Reactivity (psychology) ; Oxide ; Materials science ; Nanotechnology ; Medicine ; Metallurgy Machine Learning in Materials Science ; Graphene research and applications
Authors: Carsten Enderlein | J. Ferreira de Oliveira | M. B. Fontes | E. Baggio‐Saitovitch | Matheus Muller | S. E. Rowley | G. Cernicchiaro | Pedro Lopes | Marcello Silva Neto
Source: Research Square (preprint, 2024-04-22)
Field: Materials Science — Materials Chemistry

Emergent inductance and capacitance in elemental tellurium Tellurium ; Capacitance ; Inductance ; Materials science ; Electrical engineering ; Physics ; Engineering ; Metallurgy ; Voltage ; Quantum mechanics Phase-change materials and chalcogenides ; Advanced Semiconductor Detectors and Materials ; 2D Materials and Applications
Authors: McKinnley Bilbao | Teodoro Grau Carmona | Morgan Covarrubias | Alex Goslin | Katherine Judge | Garland Munn | Hazel Ticas | Abe Tonioli | C. Tuttle | Caitlin West | Daniel N. Scott
Source: Research Square (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

Utilizing Publicly Accessible Machine Learning for Training Student Formulators in Personal Care Product Formulation: Specification-Driven and Cost-Conscious Experiments Abstract This work explores the application of the questionable use of machine learning (ML), specifically the ChatGPT 3.5 system, in the training of student formulators. Here, an experiment is undertaken to explore the ability of ML to aid in training students in the role of formulators of a personal care product. The focus is on whether or not the students can successfully rely on ML to guide them through the formulation process of a 10-minute hydrating face mask recipe. While exploring the iterative process of recipe adjustments with ML, it was found that the language model demonstrates the ability to help formulators in training due to its above-average knowledge in chemistry, but when given clear prompts, it performs much better at providing helpful suggestions for ingredient substitutions. However, ML lacks a reliable memory, even within a single extended conversation and struggles with mathematical calculations. ML is not found to be proficient in accurately calculating cost adjustments. Additionally, the contribution of ML may only be marginally helpful in the training of more seasoned formulator. Despite its limitations, ML can quickly and effectively, in the hands of student formulators in-training, provide direction and support to produce and improve upon a base formula resulting in a quality product. Recipe ; Computer science ; Quality (philosophy) ; Process (computing) ; Conversation ; Product (mathematics) ; Training (meteorology) ; Artificial intelligence ; New product development ; Base (topology) ; Machine learning ; Psychology ; Mathematics ; Marketing ; Communication ; Business Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Machine Learning and Data Classification
Authors: Ikechukwu Lucky Eze | Felix Ahamefule Onyegbule | Christopher Obodike Ezugwu | Johnson V. Chibuzor | Omoirri Moses Aziakpono
Source: Journal of Pharmaceutical Research International (article, 2024-12-29)
Field: Chemistry — Organic Chemistry

Phytochemical and Antioxidant Evaluations of Chromolaena odorata and Huntaria umbellata With the increasing demand for herbal medicine, it is imperative to validate the presence of the most anticipated antioxidants present in plants, which motivated the aim of this study. Hence, the efficacy of Chromolaena odorata and Hunteria umbellata were validated. Natural antioxidants in medicinal plants, such as polyphenols and flavonoids, mitigate oxidative stress and serve as alternative treatments for various diseases. We obtained crude extracts through cold maceration and qualitative and quantitative phytochemical screenings for alkaloids, flavonoids, tannins, and saponins. Antioxidant profiles were assessed using DPPH and FRAP assays for In vitro and MDA, SOD, and CAT assays for In vivo. The liquid (ethanol) extracts from these plants were tested to confirm the presence of alkaloids, flavonoids, saponins, tannins, and glycosides in the ethanol extracts of Chromolaena odorata leaves and Hunteria umbellata seeds. The decreasing effect of DPPH experienced in both plant extracts validates the presence of free radical scavenging activities and ferric-reducing antioxidant potential in a concentration-dependent manner. Chromolaena odorata demonstrated superior inhibition in both DPPH (88.89 ± 0.26%) and FRAP (90.16 ± 0.76%) assays compared to Hunteria umbellata. In vivo studies showed dose-dependent antioxidant effects, with 500 mg/kg of Chromolaena odorata exhibiting the best results across MDA, SOD, and CAT indices. This study concludes that Chromolaena odorata leaves possess higher antioxidant activity and phytochemical content than Hunteria umbellata seeds, supporting their potential use in antioxidant therapies. Chromolaena odorata ; Phytochemical ; Traditional medicine ; Antioxidant ; Biology ; Botany ; Medicine ; Weed Plant-Derived Bioactive Compounds ; Phytochemicals and Antioxidant Activities ; Sesquiterpenes and Asteraceae Studies
Authors: Ambeu-Loko N’ta Christelle Mélissa | Camara Mahama | Coulibaly Souleymane | Ouattara Logopho Hyacinthe | Stéphanie Kra | Gnaly Prisca | Fanté Bamba | Kassi Amian Brise Benjamin | Cédric Logé | Jean‐Michel Robert
Source: Chemical Science International Journal (article, 2024-11-29)
Field: Chemistry — Organic Chemistry

Synthesis of New Dihydropyrimidine-2-Thione Derivatives and Their Antibacterial Screening In this work, we reported the synthesis of new dihydropyrimidine-2-thione derivatives 5a-h. The reaction intermediates of these derivatives are chalcones or 1,3-diarylprop-2-en-1-one derivatives 3a-h, synthesized from acetophenone and various aldehydes which have not yet been used for the synthesis of dihydropyrimidine-2-thiones. All sixteen (16) compounds synthesized were characterized by 1H and 13C NMR. The three (3) bacterial strains tested on the synthesized compounds were Staphylococcus aureus, Klebsiella aerogenes formerly called Enterobacter aerogenes and Pseudomonas aeruginosa. Antibacterial activity was evaluated using the agar diffusion method with a sterile disc impregnated by the compound tested at three concentrations C1 (12.5 mg/mL), C2 (6.25 mg/mL) and C3 (3.12 mg/mL). The antibiotics used as references were cefoxitin, fusidic acid, ceftazidime, imipenem and pefloxacin. Compounds 3g, 3h, 5b, 5d and 5f revealed antibacterial activity only against the Staphylococcus aureus strain. Chemistry ; Combinatorial chemistry Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Quinazolinone synthesis and applications
Authors: Andriy Galstyan | Yurii Vashkurak | Volodymyr Reutskyy | Volodymyr Starchevskyy | Andrii Burda
Source: Chemistry & Chemical Technology (article, 2024-12-20)
Field: Chemistry — Physical and Theoretical Chemistry

Study on the Liquid Phase Reaction of Ozone with Ethylbenzene and Its Derivatives This work deals with the investigation of the reactions of ethylbenzene and its derivatives with ozone in a solution of glacial acetic acid. The products and kinetic features of oxidation were studied, the kinetic parameters of the reactions were found, and the mechanism of ozonation in the liquid phase was proposed. The influence of deactivating substituents (-Br, -NO2) in the aromatic ring on the selectivity of the side chain oxidation of the substrate was established. It is shown that the main direction of ethylbenzene oxidation is ozonolysis with the formation of aliphatic compounds, while the ozonation of p-bromomethylbenzene and p-nitroethylbenzene significantly increases the selectivity of the side chain oxidation of the substrate, and the corresponding acetophenones become the main products of the reaction. The kinetics of the reaction were studied using gas chromatography and spectrophotometry, measuring the optical density of the gas flow in the UV region. Ethylbenzene ; Ozone ; Liquid phase ; Phase (matter) ; Chemistry ; Environmental chemistry ; Organic chemistry ; Chromatography ; Catalysis ; Thermodynamics ; Physics Chemical Reactions and Mechanisms ; Atmospheric Ozone and Climate ; Chemical Thermodynamics and Molecular Structure
Authors: Malalaniaina Rakotobé | Chiara Zurzolo
Source: médecine/sciences (review, 2024-11-01)
Field: Materials Science — Materials Chemistry

Les <i>tunneling nanotubes</i> Tunneling nanotubes (TNTs) are open membranous protrusions that allow direct communication between distant cells. Recent research has revealed their significant biological roles, prompting a reassessment of many physiological and pathological processes, especially in the nervous system where TNT properties could play a key physiological role. TNT-like connections have been observed in the developing brain and are implicated in neurodegenerative diseases, brain cancers, as well as in other diseases, underscoring their importance in pathophysiological events. This review covers the key features of TNTs, including their structural properties, formation mechanisms, and detection challenges. We also explore their functions, focusing on the nervous system. The discovery of TNTs may lead to a reconsideration of brain function as a physically connected neuronal network, as proposed by Golgi, complementing Cajal's theory of neurons as separate entities. Quantum tunnelling ; Materials science ; Nanotechnology ; Physics ; Condensed matter physics Carbon Nanotubes in Composites ; Graphene research and applications ; Graphene and Nanomaterials Applications
Authors: Chen Tian | Zhiping Zhu | Jianping Liao | Zhifeng Liu | Fan Gao | Yufei Chen | Zhenggang Wang
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

The study of the electrical properties of nano insulating oil for submarine cables At present, the alkylbenzene insulation oil for submarine cables of 220kV and above in China relies on imports, which poses high maintenance costs and supply interruption risks. Nano modified insulating oil has excellent electrical properties and broad application prospects in power systems. Therefore, nano SiO 2 modified insulating oil was prepared, and the effects of APTES modified and unmodified nano SiO 2 on the viscosity, breakdown voltage, and dielectric loss factor of the insulating oil were compared. The results showed that the viscosity of alkylbenzene insulating oil did not change significantly after the addition of nanoparticles, which met the operating standards for submarine cable oil; The breakdown voltage of the modified nano insulating oil has significantly increased, with a breakdown voltage of 0.12g/L being the highest. The breakdown voltage of the unmodified nano insulating oil has significantly decreased. The dielectric loss factors of modified and unmodified nano insulating oils do not vary significantly. This article provides theoretical and practical references for the domestic substitution research of submarine cable insulation oil. Submarine ; Nano- ; Materials science ; Composite material ; Engineering ; Marine engineering High voltage insulation and dielectric phenomena ; Fiber-reinforced polymer composites ; Diamond and Carbon-based Materials Research
Authors: Ryan Roussel | Juan Pablo Gonzalez-Aguilera | Auralee Edelen | Eric Wisniewski | Alex Ody | Wanming Liu | Y. K. Kim | John Power
Source: arXiv (Cornell University) (preprint, 2024-04-16)
Field: Materials Science — Materials Chemistry

Efficient 6-dimensional phase space reconstruction from experimental measurements using generative machine learning Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using conventional diagnostic techniques necessitates hundreds of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to substantially reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena, and as feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed 6-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that for a collection of synthetic beam distribution test cases that this approach can be used to resolve 6-dimensional phase space distributions using basic beam manipulations and as few as 20 2-dimensional measurements of the beam profile, without the need for prior data collection or model training. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75x faster than previous methods. Generative grammar ; Computer science ; Phase space ; Space (punctuation) ; Artificial intelligence ; Machine learning ; Algorithm ; Pattern recognition (psychology) ; Physics Solidification and crystal growth phenomena ; Magnetic Properties and Applications ; Advanced X-ray Imaging Techniques
Authors: H. Alperen Bulut | Nihan Gönülol
Source: Turkiye Klinikleri Journal of Dental Sciences (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Investigation of Water Absorption and Water Solubility of New Generation Bulk-Fill Resin Composites: in vitro Study Amaç: Çalışmanın amacı, yeni nesil bulk-fill kompozitlerin distile suda bekletilmesi sonrasında su emilimi ve çözünürlük değerlerinin incelenmesidir. Gereç ve Yöntemler: Çalışmada bir mikrohibrit kompozit (Filtek Z250), bir akışkan kompozit (Filtek Supreme Flowable), iki yüksek viskoziteli bulk-fill kompozit (Tetric Powerfill, Filtek One Bulk-Fill) ve iki düşük viskoziteli bulk-fill kompozit (Tetric Powerflow, Filtek Bulk-Fill Flowable) kullanılarak 104 örnek (8 mm çapx2 mm kalınlık) hazırlandı. Örnekler BluePhase PowerCure polimerizasyon cihazının standart modunda (1.200 mW/cm2 ) 10 sn polimerize edildi. Tetric Powerfill ve Tetric Powerflow kompozitlerinden yeni örnekler hazırlanarak aynı cihazın 3s Powercure modunda (3.050 mW/cm2 ) 3 sn polimerize edilerek 2 grup daha oluşturuldu (n=13). 30. günün sonunda su emilimi, çözünürlük ve kütle değişim yüzdeleri ISO 4049:2009 standardizasyonuna göre hesaplandı. Verilerin analizinde iki yönlü varyans analizi ve Tukey çoklu karşılaştırma testi kullanıldı. Bulgular: Gruplar arasında en düşük su emilimi Tetric Powerflow 10s grubunda görülürken en yüksek su emilimi Filtek One Bulk-Fill grubunda görüldü. Tetric Powerfill ve Tetric Powerflow kompozitlerin 10s ve 3s grupları arasında su emilimi ve çözünürlük değerleri açısından istatistiksel farka rastlanmadı (p>0,05). Tüm kompozit gruplarının kütle değişim yüzdeleri 30. güne kadar artış gösterdi. Sonuç: Tetric Powerfill ve Tetric Powerflow kompozitlerin hem standart modda hem de yüksek ışık gücü modundaki kısa süreli polimerizasyonları (3 sn) su emilim ve çözünürlük açısından dezavantaj oluşturmamıştır. Absorption of water ; Composite material ; Solubility ; Materials science ; Absorption (acoustics) ; Chemistry ; Organic chemistry Natural Fiber Reinforced Composites