jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Katarina Bobić | Ana Todorović | Snežana Pejić | Filip Veljković | Suzana Veličković | Snežana B. Pajović | Dunja Drakulić
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

TiO2 nanoparticles and TiO2 nanoparticles surface modified with salicylic acid affect neurological functions and oxidative stress markers in the eyes of adult rats Titanium dioxide nanoparticles (TiO2NPs) are widely used in pharmaceutics, food products and cosmetics. Besides numerous beneficial outcomes, they also induce toxic effects in different organs, including the eyes. The recent findings report that some surface modifications can attenuate TiO2NPs toxicity. Thus, the aim of this study was to compare the effects of acute oral treatment with commercially available, bare TiO2NPs and TiO2NPs surface modified with salicylic acid (SA-TiO2NPs) using adult Wistar rats as model organisms. Control animals were gastrically intubated with vehicle (0.01 M HCl), while others were treated with either TiO2NPs or SA-TiO2NPs (1000mg/kg dissolved in vehicle). Afterward, the mortality rate as the acute toxicity parameter and parameters of neurological function were assessed. Animals were sacrificed on the 14th day following treatment. In the eye crude synaptosomal fraction, the effects of treatments on oxidative stress markers such as prooxidant/antioxidant balance (PAB) and lipid peroxidation (LPO) were compared. According to the obtained results, the mortality was 0 in all experimental groups. TiO2NPs and SA-TiO2NPs treatments displayed mild effects on spontaneous activity and pacing and had no impact on the visual placing reflex. Moreover, TiO2NPs and SA-TiO2NPs exposure significantly elevated PAB levels, when compared to controls. Animals treated with SA-TiO2NPs had similar LPO levels as controls, while in TiO2NPs group LPO levels were significantly increased. The presented data showed that both examined nanoparticles exerted similar outcomes referring to most of the investigated parameters, however, surface binding of SA decreased the level of LPO and thus, mitigated some of TiO2NPs toxicity in the eyes. Oxidative stress ; Medicine ; Salicylic acid ; Pharmacology ; Toxicity ; Nanoparticle ; Acute toxicity ; Chemistry ; Biochemistry ; Internal medicine ; Nanotechnology Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis
Authors: Xiangyan Miao | Xinbo Yang | Yuchuan Li | Siping Pang | Khadiga Dahan | Ying Li | Juan Xu | Caixia Kan | Dennis Tokaryk | M Karplus | W Miller | E Neher | H Schwarz | J Simons | G Somorjai | R Zare | B Albinsson | H Kondoh | J Zhang | C Adamo | M Devries | C Diaz | S George | D Ghosh | K Holmberg | G Meijer | G Patwari | M Rodgers | S Sampath | M Suhm | A Troisi | Matthew Brett | Michael Price | Calum Gordon | Karen Thorn | Lara Browne | Paul Hume | Justin Hodgkiss | Bridget Stocker | Mattie Timmer | Nathaniel Davis | Jintong Zhu | Songrui Wei | Yang Cao | Wei Huang | Zhongxin Liu | Jieqiong Wang | * | Yijun Shen | ; | Xuan-Hao Cao | Zhi-Qiang Fan | Dan Wu | Soumya Mondal | Ayan Datta | Wei Wang | Dongyi Xiao | Shu- ; R | Debra Rathnayake | Sylvie Searles | Nathalie Descartes | Fernand Bouscharain | Matteo Chassagneux | Yamaldi Martini | Catherine Midiladji Bakary | David Journet | ; Philippon | Jelena Belic | Arno Fo rster | Jan Menzel | Francesco Buda | Lucas Visscher
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 18503 DOI: 10.1039/D3CP90153J This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Attribution ; Chromatin structure remodeling (RSC) complex ; Computer science ; Library science ; World Wide Web ; Chemistry ; Computer security ; Psychology ; Social psychology Luminescence Properties of Advanced Materials ; Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques
Authors: Widad H. Albanda | D. J. Fakralden | Nagwa Hassan
Source: East European Journal of Physics (article, 2023-06-02)
Field: Materials Science — Polymers and Plastics

Enhancement in Some Physical Properties of (PVP: CMC) Blend by the addition of MgO This research study explores the effects of adding MgO nanoparticles to a polymeric blend composed of 50% PVP and 50% CMC. The blend was prepared using MgO nanoparticles (0.2%, 4%, and 6%) and varying processing conditions. The structural, optical, and electrical properties of the resulting blend were analyzed to determine the impact of the added nanoparticles on the blend's properties. Results showed that the addition of nanoparticles significantly improved the structural, optical, and electrical properties of the polymeric blend. Specifically, the energy gap is 4.224 eV for (PVA: CMC) film and increased to 3.432 eV for (PVA: CMC-6% MgO), the light transmission and reflection properties were enhanced. Additionally, the conductivity of the blend was increased, making it suitable for various applications, including optoelectronics, sensors, and biomedical devices. Overall, this study demonstrates the potential of adding nanoparticles to polymeric blends to improve their properties and highlights the importance of optimizing processing conditions to achieve the desired properties for specific applications. Materials science ; Nanoparticle ; Polymer blend ; Chemical engineering ; Composite material ; Nanotechnology ; Polymer ; Copolymer Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Roman M. Dickey | Michaela A. Jones | Neil D. Butler | Ishika Govil | Aditya M. Kunjapur
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-05-02)
Field: Materials Science — Biomaterials

Genome engineering allows selective conversions of terephthalaldehyde to multiple valorized products in bacterial cells Abstract Deconstruction of polyethylene terephthalate (PET) plastic waste generates opportunities for valorization to alternative products. We recently designed an enzymatic cascade that could produce terephthalaldehyde (TPAL) from terephthalic acid. Here, we showed that the addition of TPAL to growing cultures of Escherichia coli wild-type strain MG1655 and an engineered strain for reduced aromatic a ldehyde re ction (RARE) strain resulted in substantial reduction. We then investigated if we could mitigate this reduction using multiplex automatable genome engineering (MAGE) to create an E. coli strain with 10 additional knockouts in RARE. Encouragingly, we found this newly engineered strain enabled a 2.5-fold higher retention of TPAL over RARE after 24h. We applied this new strain for the production of para -xylylenediamine (pXYL) and observed a 6.8-fold increase in pXYL titer compared to RARE. Overall, our study demonstrates the potential of TPAL as a versatile intermediate in microbial biosynthesis of chemicals that derived from waste PET. Strain (injury) ; Polyethylene terephthalate ; Escherichia coli ; Terephthalic acid ; Materials science ; Chemistry ; Biochemistry ; Polyester ; Biology ; Gene ; Composite material biodegradable polymer synthesis and properties ; Microbial Metabolic Engineering and Bioproduction ; Microplastics and Plastic Pollution
Authors: Ziyi Chen | Fankai Xie | Meng Wan | Yang Yuan | Miao Liu | Zongguo Wang | Sheng Meng | Yangang Wang
Source: arXiv (Cornell University) (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

MatChat: A Large Language Model and Application Service Platform for Materials Science The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence (GAI), including automated text generation and question-answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13,878 pieces of structured material knowledge data. This specialized AI model, named MatChat, focuses on predicting inorganic material synthesis pathways. MatChat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although MatChat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in the field of materials science. MatChat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science. Computer science ; Software deployment ; Generative grammar ; Field (mathematics) ; Data science ; Process (computing) ; Service (business) ; Artificial intelligence ; Software engineering ; Programming language Machine Learning in Materials Science ; Topic Modeling
Authors: Jang-Hee Lee | Seungsoo Jang | Min-Jae Lee | Woo‐Sung Cho | Joo-Yeon Kim | Sangsoo Han | Sung Gyun Shin | Sun Young Lee | Dae Hyuk Jang | Miyong Yun | Song Hyun Kim
Source: Journal of Radiation Protection and Research (article, 2023-12-30)
Field: Materials Science — Materials Chemistry

A Preliminary Study on Evaluation of Time-Dependent Radionuclide Removal Performance Using Artificial Intelligence for Biological Adsorbents Background: Recently, biological adsorbents have been developed for removing radionuclides from radioactive liquid waste due to their high selectivity, eco-friendliness, and renewability. However, since they can be damaged by radiation in radioactive waste, a method for estimating the bio-adsorbent performance as a time should consider the radiation damages in terms of their renewability. This paper aims to develop a simulation method that applies a deep learning technique to rapidly and accurately estimate the adsorption performance of bio-adsorbents when inserted into liquid radioactive waste.Materials and Methods: A model that describes various interactions between a bio-adsorbent and liquid has been constructed using numerical methods to estimate the adsorption capacity of the bio-adsorbent. To generate datasets for machine learning, Monte Carlo N-Particle (MCNP) simulations were conducted while considering radioactive concentrations in the adsorbent column.Results and Discussion: Compared with the result of the conventional method, the proposed method indicates that the accuracy is in good agreement, within 0.99% and 0.06% for the R&lt;sup&gt;2&lt;/sup&gt; score and mean absolute percentage error, respectively. Furthermore, the estimation speed is improved by over 30 times.Conclusion: Note that an artificial neural network can rapidly and accurately estimate the survival rate of a bio-adsorbent from radiation ionization compared with the MCNP simulation and can determine if the bio-adsorbents are reusable. Adsorption ; Radionuclide ; Radioactive waste ; Monte Carlo method ; Artificial neural network ; Computer science ; Environmental science ; Materials science ; Process engineering ; Biological system ; Nuclear engineering ; Chemistry ; Mathematics ; Statistics ; Nuclear chemistry ; Engineering ; Artificial intelligence ; Physics ; Nuclear physics Graphite, nuclear technology, radiation studies ; Radioactive element chemistry and processing ; Chemical Synthesis and Characterization
Authors: Si-Na Gu | Zhi-Ying Ou | Tingting Wu | Ting-Yu Nie | Yang Liu
Source: Zeitschrift für Kristallographie - New Crystal Structures (article, 2023-09-04)
Field: Chemistry — Inorganic Chemistry

Crystal structure of poly[diaqua-(bis(m<sub>2</sub>-1,4-bis(imidazol-1-ylmethyl)benzene)-<i>κ</i> <sup>2</sup> <i>N</i>,<i>N</i>′-manganese] dichloride, C<sub>28</sub>H<sub>32</sub>MnN<sub>8</sub>O<sub>2</sub>Cl<sub>2</sub> Abstract C 28 H 32 MnN 8 O 2 Cl 2 , triclinic, <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:mrow> <m:mi>P</m:mi> <m:mover accent="true"> <m:mn>1</m:mn> <m:mo>‾</m:mo> </m:mover> </m:mrow> </m:math> $P\overline{1}$ (no. 2), a = 8.987(4) Å, b = 9.061(3) Å, c = 10.706(2) Å, α = 88.65(2)°, β = 66.12(2)°, γ = 89.89(2)°, V = 796.9(5) Å 3 , Z = 1, R gt ( F ) = 0.1033, w R ref ( F 2 ) = 0.2218, T = 293(2) K. Triclinic crystal system ; Benzene ; Manganese ; Crystal structure ; Crystallography ; Chemistry ; Crystal (programming language) ; Stereochemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Otilija Keta | Vladana Petković | Neda Đorđević | Miloš Đorđević | Branka Ivković | Vladimir Dobričić
Source:  (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

The effects of a selected methoxy substituted chalcone in human melanoma cells irradiated with γ-rays Given the well-established potential of chalcones in modulating the response of cancer cells to therapeutic interventions, coupled with the growing imperative to enhance their biological attributes, the objective of this study was to synthesize a methoxy-substituted chalcone (OCH3) and assess its capacity to amplify the inhibitory effects of radiation in melanoma cells known for their resistance to radiotherapy. The A375 melanoma cells were subjected to a clinically relevant dose of 2 Gy gamma irradiation. OCH3 was employed either as a standalone treatment or in conjunction with irradiation. The obtained results unveiled the substantial radiosensitizing potential of OCH3 within this specific cell line. Our subsequent investigations will be designed to investigate the underlying mechanisms that contribute to the radiosensitizing properties of OCH3. Moreover, we intend to evaluate the efficacy of OCH3 against other types of radioresistant cancer cells. The presented data not only illuminates the enhanced therapeutic possibilities offered by OCH3 but also highlights its potential as a valuable agent in addressing a wider array of challenging malignancies. Radioresistance ; Melanoma ; Chalcone ; Cancer research ; Irradiation ; Radiation therapy ; Cancer ; Cancer cell ; Cell culture ; Medicine ; Chemistry ; Biology ; Internal medicine ; Physics ; Stereochemistry Synthesis and biological activity ; Synthesis and Biological Evaluation ; Quinazolinone synthesis and applications
Authors: Francesco Guidarelli Mattioli | Francesco Sciortino | John Russo
Source: arXiv (Cornell University) (preprint, 2023-01-27)
Field: Materials Science — Materials Chemistry

A neural network potential with self-trained atomic fingerprints: a test with the mW water potential We present a neural network (NN) potential based on a new set of atomic fingerprints built upon two- and three-body contributions that probe distances and local orientational order respectively. Compared to existing NN potentials, the atomic fingerprints depend on a small set of tuneable parameters which are trained together with the neural network weights. To tackle the simultaneous training of the atomic fingerprint parameters and neural network weights we adopt an annealing protocol that progressively cycles the learning rate, significantly improving the accuracy of the NN potential. We test the performance of the network potential against the mW model of water, which is a classical three-body potential that well captures the anomalies of the liquid phase. Trained on just three state points, the NN potential is able to reproduce the mW model in a very wide range of densities and temperatures, from negative pressures to several GPa, capturing the transition from an open random tetrahedral network to a dense interpenetrated network. The NN potential also reproduces very well properties for which it was not explicitly trained, such as dynamical properties and the structure of the stable crystalline phases of mW. Artificial neural network ; Simulated annealing ; Test set ; Computer science ; Set (abstract data type) ; Statistical physics ; Artificial intelligence ; Pair potential ; Biological system ; Algorithm ; Pattern recognition (psychology) ; Materials science ; Physics ; Thermodynamics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; High-pressure geophysics and materials
Authors: Thomas Zwick | Vadim Issakov | Friedel Gerfers | Ilona Rolfes | Jan Barowski | Amelie Hagelauer | Christian Waldschmidt | Christina Bonfert | Marlene Harter | Amelie Hagelauer Eumc | Tpc Chair | Ulrich Lewark | Marlene Chair | Harter | Julia-Marie Kszegi | Wolfgang Heinrich | Andreas Wentzel | Nils Pohl | Jan Wessel | Marcel Runge | Michael Gadringer | Benjamin Nuss | Martin Kunert | Jean-Christophe Nallatamby | Rdiger Quay | Christoph Scheytt | Patrick Schuh | Hermann Schumacher | Ho-Jin Song | Frank Van De Bogaart | Frank Van Vliet | Hua Wang | Lars-Erik Wernersson | Herbert Zirath | Laura Anitori | Michael Antoniou | An- Dr Bourdoux | Thomas Dallmann | Jacco De Wit | Reinhard Feger | Laurent Ferro-Famil | Marina Gashinova | Maria Greco | Stephen Har- Man | Juergen Hasch | Willem Hol | Stphane Kemkemian | Jerzy Kowalewski | Pierfrancesco Lombardo | Marco Martorella | Stphane Mric | Mayazzurra Ruggiano | Hasan Sharifi | Claudia Vasanelli | Matthias Weiss | Felix Yanovsky | Alex Yarovoy | Elena Abdo-Snchez | Edward Ackerman | Giuseppe Addamo | Janne Aikio | Beatriz Aja | Mohamad Alabd | Lutfi Albasha | Federico Alimenti | Rozenn Allanic | Jos-Ignacio Alonso | Arokiaswami Alphones | Giando- Menico Amendola | Cristina Andrei | Iltcho Angelov | Francesca Apollonio | Fritz Arndt | Holger Arthaber | Herv Aubert | Klaus Aufinger | Shmuel Auster | Damienne Bajon | Denis Barataud | Pawe_ Barmuta | Barquinero Gmez | Filipe Barradas | Ju- Raj Bartolic | Francesca Benassi | Brahim Benbakhti | Jessi- Ca Benedicto | Johannes Benedikt | Olof Bengtsson | Mark Bentum | Tibor Berceli | Eric Bergeault | Manfred Berroth | Alexandre Bessemoulin | Akanksha Bhutani | Stphane Bila | Rodrigo Blazquez-Garcia | Hans-Ludwig Bloecher | Pierre Blondy | Luigi Boccia | Georg Boeck
Source:  (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

EuMC 2023 Conference Programme Computer science Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Gaussian Processes and Bayesian Inference
Authors: Paolo Pegolo | Stefano Baroni | Federico Grasselli
Source: arXiv (Cornell University) (preprint, 2023-05-17)
Field: Chemistry — Electrochemistry

Self-interaction and transport of solvated electrons in molten salts The dynamics of (few) electrons dissolved in an ionic fluid--as when a small amount of metal is added to a solution while upholding its electronic insulation--manifests interesting properties that can be ascribed to nontrivial topological features of particle transport (e.g., Thouless' pumps). In the adiabatic regime, the charge distribution and the dynamics of these dissolved electrons are uniquely determined by the nuclear configuration. Yet, their localization into effective potential wells and their diffusivity are dictated by how the self-interaction is modeled. In this article, we investigate the role of self-interaction in the description of localization and transport properties of dissolved electrons in non-stoichiometric molten salts. Although the account for the exact (Fock) exchange strongly localizes the dissolved electrons, decreasing their tunneling probability and diffusivity, we show that the dynamics of the ions and of the dissolved electrons are largely uncorrelated, irrespective of the degree to which the electron self-interaction is treated, and in accordance with topological arguments. Electron ; Chemical physics ; Ionic bonding ; Solvated electron ; Ion ; Thermal diffusivity ; Diffusion ; Chemistry ; Adiabatic process ; Charged particle ; Materials science ; Physics ; Thermodynamics ; Physical chemistry ; Aqueous solution ; Quantum mechanics Electrochemical Analysis and Applications ; Ionic liquids properties and applications ; Spectroscopy and Quantum Chemical Studies
Authors: Wenqian Dong | Jie Ren
Source:  (article, 2023-08-10)
Field: Materials Science — Materials Chemistry

AutoConstruct: Automated Neural Surrogate Model Building and Deployment for HPC Applications Scientific Machine Learning (SciML), aiming at using machine learning methods to solve scientific computing problems, has been used in a wide range of HPC applications to improve the applications' performance. However, domain scientists, despite their rich expertise in their respective fields, often lack adequate knowledge in machine learning and computer systems. This gap makes it difficult for them to determine which computations can be replaced by neural network (NN) surrogates. Moreover, NN surrogates in HPC applications typically have sparse input features, necessitating system-level optimization to prevent memory resource overuse on accelerators. Consequently, constructing and deploying NN surrogates for HPC applications remains a significant challenge, with no existing tools to assist domain scientists in systematically building high-quality ML surrogates. To address these issues, we introduce AutoConstruct, a comprehensive solution for SciML in distributed systems. AutoConstruct consists of two components, the model-maker and deployment-helper, which operate automatically to select the appropriate ML surrogate architecture and deploy the surrogate model training and inference on specific accelerators, respectively. We showcase AutoConstruct's adaptability and performance through a series of HPC numerical benchmarks, real-world scientific applications, and computing platforms. Computer science ; Software deployment ; Adaptability ; Supercomputer ; Artificial neural network ; Artificial intelligence ; Machine learning ; Domain (mathematical analysis) ; Inference ; Deep learning ; Resource (disambiguation) ; Computer architecture ; Distributed computing ; Software engineering ; Operating system Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Model Reduction and Neural Networks