jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hatem Helal | Andrew Fitzgibbon
Source: arXiv (Cornell University) (preprint, 2024-06-05)
Field: Materials Science — Surfaces, Coatings and Films

MESS: Modern Electronic Structure Simulations Electronic structure simulation (ESS) has been used for decades to provide quantitative scientific insights on an atomistic scale, enabling advances in chemistry, biology, and materials science, among other disciplines. Following standard practice in scientific computing, the software packages driving these studies have been implemented in compiled languages such as FORTRAN and C. However, the recent introduction of machine learning (ML) into these domains has meant that ML models must be coded in these languages, or that complex software bridges have to be built between ML models in Python and these large compiled software systems. This is in contrast with recent progress in modern ML frameworks which aim to optimise both ease of use and high performance by harnessing hardware acceleration of tensor programs defined in Python. We introduce MESS: a modern electronic structure simulation package implemented in JAX; porting the ESS code to the ML world. We outline the costs and benefits of following the software development practices used in ML for this important scientific workload. MESS shows significant speedups n widely available hardware accelerators and simultaneously opens a clear pathway towards combining ESS with ML. MESS is available at https://github.com/graphcore-research/mess. Computer science Electron and X-Ray Spectroscopy Techniques
Authors: Junming Han | Tong Li | Yun He | Ziyi Yang
Source: Research Square (preprint, 2024-02-20)
Field: Chemistry — Analytical Chemistry

Predicting the properties of fruit chemicals using neural networks Abstract As a kind of machine learning method, neural networks have made significant breakthroughs in various fields in the past few years, especially in the fields of text, speech, images, videos, etc., where data is unstructured, traditional machine learning hardly exceeded ’glass ceiling’, So many researchers have turned to using neural networks as auxiliary tools, achieve significant breakthroughs, or found new research methods. In the computational chemistry field, neural networks have ubiquity and broad applicability to a range of challenges, including virtual screening, quantitative structure-activity relationship, protein structure prediction, materials design, quantum chemistry, and property prediction. So, this paper proposes an efficient way that use graph neural networks to predict the properties of chemicals in fruits, and this research aims to provide some guidance to researchers and shorten the cycle of identifying chemicals. Artificial neural network ; Biochemical engineering ; Chemistry ; Biological system ; Computer science ; Artificial intelligence ; Biology ; Engineering Spectroscopy and Chemometric Analyses
Authors: Eda Yıldız | Nur Kuş | Selen Guner | Gülüm Şümnü
Source: GIDA / THE JOURNAL OF FOOD (article, 2024-11-14)
Field: Materials Science — Biomaterials

DEVELOPMENT OF BIODEGRADABLE ACTIVE FOOD PACKAGING FROM AGRICULTURAL WASTES: PRODUCTION, CHARACTERIZATION, AND APPLICATION OF FILMS WITH BANANA PEEL AND OLIVE LEAF EXTRACT The objective of this study is to produce a biodegradable active food package by using agricultural wastes; banana peel and olive leaf extract (OLE). To investigate the possible effect of plasticized starch at different concentrations (0%, 5%, 10%, and 15%), corn starch was added to the films. The films were analyzed in terms of physical characteristics (moisture content, swelling degree, water solubility, density, opacity), water vapor permeability, and mechanical properties. In addition, the phenolic content and antioxidant activity of the films were measured and antimicrobial activity of the films were tested on common food-borne pathogens. Films with better characteristics (B_S15_OLE) were selected to investigate the possible influence of the active films on shelf life of strawberries. Finally, it was shown that at the end of 10days, the films were almost completely decomposed. Food science ; Starch ; Food packaging ; Water content ; Shelf life ; Swelling ; Solubility ; Active packaging ; Water activity ; Moisture ; Chemistry ; Materials science ; Composite material ; Organic chemistry Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Agricultural and Food Sciences
Authors: G Raitsky | Yu Drobyazgo
Source: Material and Mechanical Engineering Technology (article, 2024-03-30)
Field: Materials Science — General Materials Science

Design of Scrubber for Sedimentation and Dissolution of Milk Dust In the work, the necessity of creating a device for settling the dispersed phase of milk dust - dry milk product, at the section of the coolant outlet to the environment is investigated. The quality of dry product deposition in the used devices: cyclones and filters has been evaluated. Technological factors occurring in scrubbers during sedimentation and dissolution of milk product are considered. The design of scrubber, providing the use of patented technology of return of precipitated dispersed phase of milk dust into a full commercial product, is offered. Scrubber ; Sedimentation ; Settling ; Dissolution ; Deposition (geology) ; Environmental science ; Waste management ; Engineering ; Environmental engineering ; Geology ; Sediment ; Chemical engineering Material Properties and Applications ; Cyclone Separators and Fluid Dynamics ; Engineering and Environmental Studies
Authors: M A Lemeshko | Irina Zanina | Eugenia Kostromina | N Salikova
Source: Material and Mechanical Engineering Technology (article, 2024-03-30)
Field: Materials Science — General Materials Science

Bench Studies of Sound Insulation Materials and Vacuum Sound Insulation Panel There is a problem of protection from high-level sound of city residents, employees of enterprises; to a greater extent, in the workshops of machine-building factories, for example, during stamping, forging and other metalworking processes. Specialists and maintenance personnel are exposed to high-intensity noise near powerful agitators, pumps, engines and other power equipment. Due to these circumstances, it is necessary to constantly improve systems, devices, materials, coatings that reduce the negative impact of high noise levels on people. For this purpose, various soundproof walls, panels, screens, plates are being developed. One of the directions of modernizing sound-proofing fences is the method of using vacuum to design these protective devices. The article discusses the use of vacuum in special sound-insulating panels developed by the authors. The information about bench experimental studies of some sound-proofing materials is given, some results of testing of vacuum soundproofing panels are presented. The research stand and research methodology are described. The features of the stand for the study of sound-proofing properties of flat samples of sound-proofing materials of the same size and thickness are given. Soundproofing ; Sound (geography) ; Noise (video) ; Sound power ; Acoustics ; Engineering ; Mechanical engineering ; Architectural engineering ; Computer science Material Properties and Applications ; Engineering Technology and Methodologies ; Construction Management and Sustainability
Authors: Andreas Sperlich | Klaus H. Eckstein | Florian Oberndorfer | Bernd K. Sturzda | Michael Auth | Vladimir Dyakonov | Roland Mitrić | Tobias Hertel
Source: arXiv (Cornell University) (preprint, 2024-03-08)
Field: Materials Science — Materials Chemistry

Onset of Spin Entanglement in Doped Carbon Nanotubes Studied by EPR Nanoscale semiconductors with isolated spin impurities have been touted as promising materials for their potential use at the intersection of quantum, spin, and information technologies. Electron paramagnetic resonance (EPR) studies of spins in semiconducting carbon nanotubes have overwhelmingly focused on spins more strongly localized by $\rm sp^3$-type lattice defects. However, the creation of such impurities is irreversible and requires specific reactions to generate them. Shallow charge impurities, on the other hand, are more readily and widely produced by simple redox chemistry, but have not yet been investigated for their spin properties. Here we use EPR to study p-doped (6,5) semiconducting single-wall carbon nanotubes (s-SWNTs) and elucidate the role of impurity-impurity interactions in conjunction with exchange and correlation effects for the spin behavior of this material. A quantitative comparison of the EPR signals with phenomenological modeling combined with configuration interaction electronic structure calculations of impurity pairs shows that orbital overlap, combined with exchange and correlation effects, causes the EPR signal to disappear due to spin entanglement for doping levels corresponding to impurity spacings of $14\,\rm nm$ (at 30 K). This transition is predicted to shift to higher doping levels with increasing temperature and to lower levels with increasing screening, providing an opportunity for improved spin control in doped s-SWNTs. Electron paramagnetic resonance ; Quantum entanglement ; Carbon nanotube ; Spin (aerodynamics) ; Materials science ; Doping ; Condensed matter physics ; Carbon fibers ; Chemical physics ; Nanotechnology ; Nuclear magnetic resonance ; Physics ; Quantum mechanics ; Composite material ; Quantum ; Thermodynamics Carbon Nanotubes in Composites ; Crystallography and Radiation Phenomena
Authors: Konstantin E. German
Source:  (article, 2024-12-11)
Field: Chemistry — Inorganic Chemistry

SORPTION OF TECHNETIUM-99 BY BASIC MINERALS AND ROCKS OF THE RADIOACTIVE WASTE DEPOSITORIES AND SOME METHODS FOR TECHNETIUM IMMOBILIZATION Shukurov Murodbek Ollamurodogly Resuscitator Children's hospital of the city of Yangiyul Safarboyev Izzat Bohodorovich Resuscitator Children's hospital of the city of YangiyulRelevance.The most common causes of prerenal acute renal failure (ARF) in children are dehydration against the background of acute gastroenteritis, accompanied by fluid loss through the gastrointestinal tract (vomiting, diarrhea), exogenous (poisoning) and endogenous (sepsis) intoxications, and the so-called "losses in the third space" (with burns, nephrotic syndrome, liver diseases).Serious consequences are oliguria / anuria.Early initiation of extracorporeal detoxification helps to prevent the development of multiple organ failure.Numerous studies have supported the concept that antihypertensive therapy slows down the rate of progression of CKD.The negative effect of the anuric phase on erythropoiesis in acute renal failure is known [1][2][3][4].However, there is not enough data to evaluate the effectiveness of anemia correction in the anuric phase in acute renal failure in children under 3 years of age, which served as the basis for this study.Keywords: anemia, acute renal Technetium ; Sorption ; Radioactive waste ; Radiochemistry ; Waste management ; Environmental science ; Nuclear chemistry ; Chemistry ; Adsorption ; Engineering Radioactive element chemistry and processing ; Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: Nabila Syifa Nurrahmah | Emma Rochima | Iis Rostini | Rusky Intan Pratama
Source: Asian Food Science Journal (review, 2024-11-18)
Field: Materials Science — Biomaterials

Gelatin, Chitosan, and Beeswax Based Edible Packaging for Food Product: A Review The widespread use of petroleum-based plastic packaging as food packaging has raised many concerns regarding the negative impact on the environment. The increase of public awareness on environmental problems and the importance of food safety has encouraged a lot of research regarding the development of environmentally friendly food packaging that is safe for food products, such as edible packaging. Gelatin, chitosan, and beeswax have attracted attention in edible packaging research due to their biodegradability and good film-forming characteristics. Chitosan and beeswax have also been reported to have antimicrobial properties that can improve the functional properties of edible packaging. Various studies have been carried out to obtain alternative packaging from a combination of these materials. This article reviews the potential application of edible packaging made from gelatin, chitosan, beeswax and their composites as packaging for food products. Beeswax ; Gelatin ; Chitosan ; Food science ; Food packaging ; Product (mathematics) ; Polymer science ; Materials science ; Chemistry ; Chemical engineering ; Engineering ; Wax ; Mathematics ; Composite material ; Organic chemistry Nanocomposite Films for Food Packaging ; Consumer Packaging Perceptions and Trends
Authors: Pavel Konovalov | А.А. Иванченкο | G.L. Larionov
Source:  (article, 2024-12-11)
Field: Materials Science — General Materials Science

EXPERIENCE OF DEVELOPMENT AND APPLICATION IN OPERATION OF SYSTEMS FOR MONITORING THE TECHNICAL CONDITION OF SHIP ICE Systems engineering ; Computer science ; Sea ice ; Environmental science ; Marine engineering ; Engineering ; Geology ; Climatology Material Properties and Applications ; Technical Engine Diagnostics and Monitoring ; Engine and Fuel Emissions
Authors: Nguyễn Văn Hiệu | Ngo Le Huy Hien | Dinh Minh Toan | Phan Van Binh | Phan Minh Nhat | P. T. N. Anh | Le Viet Hung | Nguyen Huy Tuong
Source: Cybernetics and Physics (article, 2024-12-28)
Field: Chemistry — Analytical Chemistry

NIRsViT: a novel deep learning model for manure identification using near-infrared-spectroscopy and imbalanced data handling The robust and accurate identification of different forms of manure stands as a pivotal imperative within the domain of agriculture. Near-infrared (NIR) Spectroscopy has emerged as an expeditious, efficient, non-destructive, and reliable approach to addressing this challenging task. NIR spectroscopy has the potential to serve as a valuable tool for the classification and identification of manure varieties. In order to enhance fertilizer identification performance, this study proposes a novel model called NIRsViT which classifies fertilizers by employing a combination of deep learning Vision Transformer model on NIR spectral data. The introduced model’s performance outperforms existing deep learning models, with an F1-Score of 86.42% and an accuracy rate of 95.19%. Additionally, the model’s classification performance has been significantly improved by proposed imbalanced data processing approaches, Focal Loss, and Upsample, with an F1-Score up to 93.91%, the improved F1-score proved that imbalanced data was considerably solved. The proposed method is a promising approach to handling imbalanced NIR spectral data and acts as a pioneering benchmark for subsequent models in manure identification through NIR spectroscopy. Future research gears toward improving the NIRsViT model’s temporal efficiency and computational load, while also testing the introduced imbalanced data handling approach for efficiency comparison across various models and larger datasets. Identification (biology) ; Spectroscopy ; Near-infrared spectroscopy ; Artificial intelligence ; Infrared ; Remote sensing ; Computer science ; Environmental science ; Geology ; Optics ; Physics ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Martijn Oldenhof | Edward De Brouwer | Ádám Arany | Yves Moreau
Source: arXiv (Cornell University) (preprint, 2024-04-02)
Field: Chemistry — Physical and Theoretical Chemistry

Atom-Level Optical Chemical Structure Recognition with Limited Supervision Identifying the chemical structure from a graphical representation, or image, of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet, existing methods for chemical structure recognition do not typically generalize well, and show diminished effectiveness when confronted with domains where data is sparse, or costly to generate, such as hand-drawn molecule images. To address this limitation, we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches, our method provides atom-level localization, and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking, we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency, accuracy, and atom-level entity prediction. Atom (system on chip) ; Materials science ; Business ; Computer science ; Embedded system Various Chemistry Research Topics ; Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors
Authors: Pol Febrer | Peter Bjørn Jørgensen | Miguel Pruneda | Alberto Garcia | Pablo Ordejón | Arghya Bhowmik
Source: ChemRxiv (preprint, 2024-07-04)
Field: Materials Science — Materials Chemistry

GRAPH2MAT: UNIVERSAL GRAPH TO MATRIX CONVERSION FOR ELECTRON DENSITY PREDICTION Electron density is a fundamental observable of an atomic system from which all ground state properties can be computed. As a prediction target for machine learning models, electron density is often represented on a dense real space grid, which is data heavy, or through density fitting approximations. In this work, we show the power of targeting the density matrix, a linear-scaling sparse SE(3) equivariant matrix that encodes the exact density. We introduce Graph2Mat, a universal function for converting molecular graphs into equivariant matrices. We demonstrate how a machine learning model that combines this Graph2Mat approach with state of the art molecular graph representation learning architectures can predict density matrix of molecular systems accurately, such as it achieves state-of-the-art performance surpassing even grid-based methods, while using datasets that are at least one order of magnitude smaller. Accurately predicted electronic density can also accelerate Density Functional Theory (DFT) calculations by reducing the number of self consistent field (SCF) iterations needed to converge. In particular, we get a 40\% reduction on the number of SCF steps on DFT calculations of QM9 molecules with SIESTA. The novel prediction model also allows two new and promising measures of uncertainty (total charge error and self-consistency error). These results open the door for many applications using hybrid ML-accelerated DFT methodologies, and uncertainty aware single iteration ab initio molecular dynamics. Electron ; Graph ; Matrix (chemical analysis) ; Computer science ; Theoretical computer science ; Physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Graphene research and applications
Authors: М. М. Мурадов | I.G. Huseynova | M.K. Nazarova | M.M. Gatamov | T.R. Proochochkina | A. A. Agayev
Source:  (article, 2024-12-19)
Field: Chemistry — Organic Chemistry

ELECTROCHEMICAL METHOD OF CHLORINATION OF 2,4-DIMETHYLPHENOL - Electrochemistry ; Computer science ; Materials science ; Chemistry ; Electrode ; Physical chemistry Inorganic and Organometallic Chemistry ; Electrochemical Analysis and Applications
Authors: A.M. Kaskanova | К. І. Проскуріна | S.D. Belkina | Yerlan Zharykbasov
Source:  (article, 2024-12-11)
Field: Materials Science — Biomaterials

EXPLORATION OF MORPHOLOGICAL AND CHEMICAL CHARACTERISTICS OF COLLAGEN COMPONENTS ORIGINATING FROM A VARIETY OF ANIMAL SPECIES AND THEIR POSSIBLE IMPACT ON THE FINAL PRODUCTS OF THE STUDY Variety (cybernetics) ; Computer science ; Biochemical engineering ; Biological system ; Biology ; Engineering ; Artificial intelligence Collagen: Extraction and Characterization ; Engineering Technology and Methodologies
Authors: Orhan Şahmerdan | İsmet Tıkız
Source: Kocaeli Journal of Science and Engineering (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Energy and Exergy Analysis of Heat Recovery from the Accumulating Tanks of a Central Heating System by Employing a Sample of Thermoelectric Generators Heat recovery using a series of thermoelectric generator (TEG) samples improved and studied previously is investigated in this paper. For this, such TEGs are connected to the accumulating tanks (ATs) of a geothermal central heating system assisted by natural gas to recover all waste heat from those devices. The study was carried out based on energy and exergy analysis. In the energy analysis part of the study, the proposed energy conversion efficiency of the TEG sample for the temperature difference in an AT-TEG system in this current study, which is 2%, was applied to find power output and energetic efficiency. As a result, total net power production was 9.888 kW, while overall energy efficiency was 7.717%. It can be observed that this amount of net power generation is sufficient to supply 61.262% of the electrical power needed for the circulating pumps in the remaining parts of the central heating system. This research reveals that TEG application in heat recovery is a promising way forward, but there is still a need to enhance the conversion efficiency of such devices. Exergy ; Thermoelectric effect ; Sample (material) ; Thermoelectric generator ; Heat energy ; Process engineering ; Environmental science ; Energy (signal processing) ; Nuclear engineering ; Waste management ; Materials science ; Engineering ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Refrigeration and Air Conditioning Technologies ; Advanced Thermodynamic Systems and Engines