jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huasheng Sun | Kaiming Deng | Erjun Kan | Yongping Du
Source: Nanoscale Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Second-order Jahn–Teller effect induced high-temperature ferroelectricity in two-dimensional NbO<sub>2</sub>X (X = I, Br) Based on the first-principles calculations, we investigated the ferroelectric properties of two-dimensional (2D) materials NbO<sub>2</sub>X (X = I, Br). Our cleavage energy analysis shows that exfoliating one NbO<sub>2</sub>I monolayer from its existing bulk counterpart is feasible. The phonon spectrum and molecular dynamics simulations confirm the dynamic and thermal stability of the monolayer structures for both NbO<sub>2</sub>I and NbO<sub>2</sub>Br. Total energy calculations show that the ferroelectric phase is the ground state for both materials, with the calculated in-plane ferroelectric polarizations being 384.5 pC m<sup>-1</sup> and 375.2 pC m<sup>-1</sup> for monolayers NbO<sub>2</sub>I and NbO<sub>2</sub>Br, respectively. Moreover, the intrinsic Curie temperature <i>T</i><sub>C</sub> of monolayer NbO<sub>2</sub>I (NbO<sub>2</sub>Br) is as high as 1700 K (1500 K) from Monte Carlo simulation. Furthermore, with the orbital selective external potential method, the origin of ferroelectricity in NbO<sub>2</sub>X is revealed as the second-order Jahn-Teller effect. Our findings suggest that monolayers NbO<sub>2</sub>I and NbO<sub>2</sub>Br are promising candidate materials for practical ferroelectric applications. Natural bond orbital ; Ferroelectricity ; Jahn–Teller effect ; Condensed matter physics ; Order (exchange) ; Monolayer ; Materials science ; Chemistry ; Crystallography ; Physics ; Computational chemistry ; Nanotechnology ; Ion ; Quantum mechanics ; Density functional theory ; Dielectric Ferroelectric and Piezoelectric Materials ; 2D Materials and Applications
Authors: Ayende Ayende | Rini Riastuti | Johny Wahyuadi Soedarsono | Agus Paul Setiawan Kaban | Mohammad Ikbal Hikmawan | Rizal Tresna Rahmdani
Source: Eastern-European Journal of Enterprise Technologies (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Development of Annona muricata Linn as green corrosion inhibitor under produced water: inhibition performance and adsorption model This work used soursop as a green corrosion inhibitor to protect API 5L Grade A from detrimental corrodent under produced water. Despite the effectiveness of inorganic inhibitors, recent evidence on their toxicity test suggests that implementing organic inhibitors is substantial to replace synthetic corrosion inhibitors. However, soursop utilization as a green corrosion inhibitor is poorly understood due to the lack of a comprehensive extraction mode and inhibitive mechanism. Several tests were conducted, including weight loss, potentiodynamic polarization, and electrochemical impedance spectroscopy (EIS), to unveil the nature of corrosion inhibition. Fourier Transform Infra-Red Spectroscopy revealed the dominant functional groups to bind with the substrate. The potentiodynamic polarization results show that the inhibitor is a mixed-type inhibitor that influences the anodic and cathodic reactions. The weight loss test showcases the highest inhibition efficiency of 52.62 % upon adding 2 ml inhibitors upon eight observation days. The polarization and EIS results provide that the inhibitor reduces the corrosion rate with higher inhibition of 88.52 %. The mentioned result is associated with the attachment of non-polar and polar Annona muricata Linn functional groups. The primary functional group involves C=O, C‒C and –O.H., which actively bonded to the metal's surface. The aromatic group at a wavenumber of 1,050 and 1,090 cm-1 shows ether's presence and behaves as an adsorption center. In this work, combining three solvents, hexane, acetone, and ethanol, elicits the complete extraction of the predominant compound from soursop Corrosion ; Corrosion inhibitor ; Annona muricata ; Chemistry ; Adsorption ; Dielectric spectroscopy ; Fourier transform infrared spectroscopy ; Nuclear chemistry ; Organic chemistry ; Electrochemistry ; Chemical engineering ; Traditional medicine Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Haiyang Yu | Xu Zhao | Xiaofeng Qian | Xiaoning Qian | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2023-06-08)
Field: Materials Science — Materials Chemistry

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry and condensed matter physics. Efficiency and equivariance are two important, but conflicting factors. In this work, we propose a SE(3)-equivariant network, named QHNet, that achieves efficiency and equivariance. Our key advance lies at the innovative design of QHNet architecture, which not only obeys the underlying symmetries, but also enables the reduction of number of tensor products by 92\%. In addition, QHNet prevents the exponential growth of channel dimension when more atom types are involved. We perform experiments on MD17 datasets, including four molecular systems. Experimental results show that our QHNet can achieve comparable performance to the state of the art methods at a significantly faster speed. Besides, our QHNet consumes 50\% less memory due to its streamlined architecture. Our code is publicly available as part of the AIRS library (\url{https://github.com/divelab/AIRS}). Equivariant map ; Homogeneous space ; Computer science ; Hamiltonian (control theory) ; Quantum ; Architecture ; Graph ; Key (lock) ; Theoretical computer science ; Topology (electrical circuits) ; Mathematics ; Physics ; Pure mathematics ; Quantum mechanics ; Mathematical optimization ; Geometry ; Combinatorics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Daiwei Zhang | Tianci Liu | Jian Kang
Source: Stat (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Density regression and uncertainty quantification with Bayesian deep noise neural networks Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of applications. However, it remains a challenging task to accurately quantify the uncertainty in DNN predictions, especially those of continuous outcomes. To this end, we propose the Bayesian deep noise neural network (B-DeepNoise), which generalizes standard Bayesian DNNs by extending the random noise variable from the output layer to all hidden layers. Our model is capable of approximating highly complex predictive density functions and fully learn the possible random variation in the outcome variables. For posterior computation, we provide a closed-form Gibbs sampling algorithm that circumvents tuning-intensive Metropolis-Hastings methods. We establish a recursive representation of the predictive density and perform theoretical analysis on the predictive variance. Through extensive experiments, we demonstrate the superiority of B-DeepNoise over existing methods in terms of density estimation and uncertainty quantification accuracy. A neuroimaging application is included to show our model's usefulness in scientific studies. Computer science ; Bayesian probability ; Artificial neural network ; Artificial intelligence ; Gibbs sampling ; Noise (video) ; Range (aeronautics) ; Uncertainty quantification ; Regression ; Variance (accounting) ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Statistics ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Gaussian Processes and Bayesian Inference
Authors: F. Nouri | Nahla A. Alassaf
Source: METHODS AND OBJECTS OF CHEMICAL ANALYSIS (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Partial Least Squares Method for the Multicomponent Analysis of Antibacterial Mixture This study's objective is to assess how well UV spectrophotometry can be used in conjunction with multivariate calibration based on partial least squares (PLS) regression for concurrent quantitative analysis of antibacterial mixture (Levofloxacin (LIV), Metronidazole (MET), Rifampicin (RIF) and Sulfamethoxazole (SUL)) in their artificial mixtures and pharmaceutical formulations. The experimental calibration and validation matrixes were created using 42 and 39 samples, respectively. The concentration range taken into account was 0-17 μg/mL for all components. The calibration standards' absorbance measurements were made between 210 and 350 nm, with intervals of 0.2 nm. The associated parameters were examined in order to develop the optimal calibration model. The cross-validation method was used to determine the ideal number of components. The coefficient of determination (R2) and the root mean square error of calibration (RMSEC) are used to evaluate the calibration model. The relation between the LEV, MET, RIF, and SUL actual values and predicted values had a coefficient of determination that was higher than 0.997, showing very good accuracy of the devised approach. The obtained RMSEC values, 0.181056465 (LEV), 0.180375418 (MET), 0.142767171 (RIF), and 0.17157454 (SUL), show an analytical procedure with adequate precision. The suggested technique for quantitative analysis of the quaternary mixture of LEV, MET, RIF, and SUL have been applied successfully in different pharmaceutical preparations. The UV spectrophotometry assisted with chemometric-PLS without prior treatment, be utilised to resolve multicomponent mixtures successfully. Partial least squares regression ; Calibration ; Chromatography ; Chemometrics ; Principal component regression ; Absorbance ; Chemistry ; Analytical Chemistry (journal) ; Spectrophotometry ; Mathematics ; Statistics ; Linear regression Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Antibiotics Pharmacokinetics and Efficacy
Authors: Yuxing Peng | Alexander J. Pak | Aleksander E. P. Durumeric | Patrick G. Sahrmann | Sriramvignesh Mani | Jaehyeok Jin | Timothy D. Loose | Jeriann R. Beiter | Gregory A. Voth
Source: ChemRxiv (preprint, 2023-07-11)
Field: Materials Science — Materials Chemistry

OpenMSCG: A Software Tool for Bottom-up Coarse-graining The “bottom-up” approach to coarse-graining – for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes – is an important approach in computational chemistry, biophysics, and materials science. As one example, the multiscale coarse-graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure such as MS-CG modeling is particularly valuable. Here we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (ED-CG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS and NAMD. OpenMSCG is modulized in the Python programming framework, which allows users to create and customize modeling “recipes” for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications. Granularity ; Computer science ; Python (programming language) ; Software ; Computational science ; Theoretical computer science ; Data mining ; Programming language Block Copolymer Self-Assembly ; Theoretical and Computational Physics ; Protein Structure and Dynamics
Authors: Morteza Nazerian | Fatemeh Naderi | Antonios N. Papadopoulos
Source: Journal of Composites Science (article, 2023-03-03)
Field: Materials Science — Polymers and Plastics

Performance Evaluation of an Improved ANFIS Approach Using Different Algorithms to Predict the Bonding Strength of Glulam Adhered by Modified Soy Protein–MUF Resin Adhesive Despite studies on the potential replacement of synthetic resins by bio-based adhesives such as proteins in recent years, there is still no reliable method for estimating the strength of wood products made using the combined parameters in the literature. This limitation is due to the nonlinear relationship between strength and the combined components. In the present research, the application of artificial intelligence techniques was studied to predict the bonding strength of glulam adhered by protein containing different ratios of MUF (melamine–urea–formaldehyde) resin with different F-to-U/M molar ratios at different press temperatures. For this purpose, the ANFIS artificial intelligence model was used as basic mode or combined with ant colony optimization (ACOR), particle swarm optimization (PSO), differential evaluation (DE) and genetic algorithms (GA) to develop an optimal trained model to predict the bonding strength of glulam based on experimental results. Comparison of the obtained results with the experimental results showed the ability of the above methods to estimate the bonding strength of glulam in a reliable manner. Although the basic ANFIS alone and in combination with other algorithms was not able to achieve an ideal performance prediction to estimate bonding strength, the combination of GA and ANFIS offered an excellent ability compared to the combination of other algorithms combined with ANFIS. Hence, the developed ANFIS-GA model is introduced as the best prediction technique to solve bonding strength problems of laminated products. In addition, using the developed optimal model, a precise attempt was made to show the nature of the parameters used to produce glulam and determine the optimum limit. Adaptive neuro fuzzy inference system ; Particle swarm optimization ; Materials science ; Algorithm ; Adhesive ; Bonding strength ; Computer science ; Bond strength ; Composite material ; Fuzzy logic ; Artificial intelligence ; Layer (electronics) Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Lignin and Wood Chemistry
Authors: Benedetta Albini | Pietro Galinetto | Serena Schiavi | E. Giulotto
Source: Sensors (review, 2023-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Food Safety Issues in the Oltrepò Pavese Area: A SERS Sensing Perspective Handly and easy-to-use optical instrumentation is very important for food safety monitoring, as it provides the possibility to assess law and health compliances at every stage of the food chain. In particular, the Surface-enhanced Raman Scattering (SERS) method appears highly promising because the intrinsic drawback of Raman spectroscopy, i.e., the natural weakness of the effect and, in turn, of the signal, is overcome thanks to the peculiar interaction between laser light and plasmonic excitations at the SERS substrate. This fact paved the way for the widespread use of SERS sensing not only for food safety but also for biomedicine, pharmaceutical process analysis, forensic science, cultural heritage and more. However, the current technological maturity of the SERS technique does not find a counterpart in the recognition of SERS as a routine method in compliance protocols. This is mainly due to the very scattered landscape of SERS substrates designed and tailored specifically for the targeted analyte. In fact, a very large variety of SERS substrates were proposed for molecular sensing in different environments and matrices. This review presents the advantages and perspectives of SERS sensing in food safety. The focus of the survey is limited to specific analytes of interest for producers, consumers and stakeholders in Oltrepò Pavese, a definite regional area that is located within the district of Pavia in the northern part of Italy. Our attention has been addressed to (i) glyphosate in rice fields, (ii) histamine in a world-famous local product (wine), (iii) tetracycline, an antibiotic often detected in waste sludges that can be dangerous, for instance in maize crops and (iv) Sudan dyes-used as adulterants-in the production of saffron and other spices, which represent niche crops for Oltrepò. The review aims to highlight the SERS performance for each analyte, with a discussion of the different methods used to prepare SERS substrates and the different reported limits of detection. Nanotechnology ; Food safety ; Computer science ; Materials science ; Risk analysis (engineering) ; Chemistry ; Business Gold and Silver Nanoparticles Synthesis and Applications ; Dye analysis and toxicity ; Identification and Quantification in Food
Authors: Vijila Chellappan | Adithya Kumar | Saif Ali khan | Pawan Kumar | Kedar Hippalgaonkar
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Diagnosis of doped conjugated polymer films using hyperspectral imaging Hyperspectral imaging provides the ability to collect spatially and spectrally resolved absorption spectra of organic films to gauge crystallinity, doping efficiency, material composition, thickness and uniformity. Hyperspectral imaging ; Crystallinity ; Doping ; Materials science ; Conjugated system ; Polymer ; Absorption (acoustics) ; Chemical imaging ; Absorption spectroscopy ; Optics ; Analytical Chemistry (journal) ; Optoelectronics ; Remote sensing ; Environmental chemistry ; Chemistry ; Composite material ; Geology ; Physics Conducting polymers and applications ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Christian Dellen | Samuel Tardif | Ravi Raj Purohit Purushottam Raj Purohit | Jean‐Sébastien Micha | Olivier Guillon | Norbert H. Menzler
Source: ECS Transactions (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Analysis of Structural Degradation Effects in a Solid Oxide Fuel Cell after Long-Term Operation An in-depth post mortem analysis was conducted on cells from a stack with an operation time of 100,000 h. In doing so, the focus of the investigations was mainly on the air electrode side. By utilizing scanning electron microscopy in combination with energy dispersive X-ray spectroscopy the microstructure and the localized chemical composition was investigated. Additionally, the structural changes of the cells were analyzed by X-ray diffraction, by Raman spectroscopy, and by synchrotron-based µ-Laue diffraction measurements. The combination of these advanced analytical tools highlights changes in the structure and chemical composition on the air electrode side caused by various degradation effects after long-term operation. Raman spectroscopy ; Materials science ; Stack (abstract data type) ; Diffraction ; Electrode ; Degradation (telecommunications) ; Microstructure ; Oxide ; Scanning electron microscope ; Synchrotron ; Spectroscopy ; Chemical composition ; Energy-dispersive X-ray spectroscopy ; Chemical engineering ; Analytical Chemistry (journal) ; Chemistry ; Composite material ; Optics ; Metallurgy ; Electronic engineering ; Environmental chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Ramón Vilar | Timothy Kench | Arielle Rahardjo | B. Adinarayana | Thomas E. Maher | Marko Storch
Source: ChemRxiv (preprint, 2023-07-12)
Field: Chemistry — Organic Chemistry

A semi-automated, high-throughput approach for the synthesis and identification of highly photo-cytotoxic iridium complexes The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein we report on such a workflow for the synthesis and evaluation of libraries of over 80 iridium(III) complexes as phototoxic agents against cancer cells. We demonstrate that it is possible to complete the whole semiautomated workflow within 72 hours, allowing for rapid library iteration. This includes all steps from the synthesis of a library of new complexes and their characterisation, to determining their cellular uptake, localisation and phototoxicity. We demonstrate the power of this rapid synthesise-and-test cycle by identifying highly active complexes which are well-tolerated in the dark but display phototoxicity at very low nM concentrations against cancer cells. Workflow ; Phototoxicity ; Iridium ; Drug discovery ; Computer science ; Combinatorial chemistry ; Identification (biology) ; Throughput ; Chemistry ; High-throughput screening ; Computational biology ; Nanotechnology ; In vitro ; Database ; Biochemistry ; Materials science ; Biology ; Catalysis Click Chemistry and Applications ; Computational Drug Discovery Methods ; Advanced Biosensing Techniques and Applications
Authors: Chengmeng Wang | Peng Jin | Fubao Yang | Liujun Xu | Jiping Huang
Source: arXiv (Cornell University) (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

Click Metamaterials: Fast Acquisition of Thermal Conductivity and Functionality Diversities Material science is an important foundation of modern society development, covering significant areas like chemosynthesis and metamaterials. Click chemistry provides a simple and efficient paradigm for achieving molecular diversity by incorporating modified building blocks into compounds. In contrast, most metamaterial designs are still case by case due to lacking a fundamental mechanism for achieving reconfigurable thermal conductivities, largely hindering design flexibility and functional diversity. Here, we propose a universal concept of click metamaterials for fast realizing various thermal conductivities and functionalities. Tunable hollow-filled unit cells are constructed to mimic the modified building blocks in click chemistry. Different hollow-filled arrays can generate convertible thermal conductivities from isotropy to anisotropy, allowing click metamaterials to exhibit adaptive thermal functionalities. The straightforward structures enable full-parameter regulation and simplify engineering preparation, making click metamaterials a promising candidate for practical use in other diffusion and wave systems. Metamaterial ; Click chemistry ; Thermal conductivity ; Isotropy ; Materials science ; Computer science ; Flexibility (engineering) ; Thermal ; Nanotechnology ; Optoelectronics ; Physics ; Mathematics ; Optics ; Composite material Thermal properties of materials ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices