jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Liu | Mohan Chen
Source: arXiv (Cornell University) (preprint, 2024-10-17)
Field: Materials Science — Materials Chemistry

Multihyperuniformity in high entropy MXenes MXenes are a large family of two-dimensional transition metal carbides and nitrides that possess excellent electrical conductivity, high volumetric capacitance, great mechanical properties, and hydrophilicity. In this work, we generalize the concept of multihyperuniformity (MH), an exotic state that can exist in a disordered multi-component system, to two-dimensional materials MXenes. Disordered hyperuniform systems possess an isotropic local structure that lacks traditional translational and orientational order, yet they completely suppress infinite-wavelength density fluctuations as in perfect crystals and, in this sense, possess a hidden long-range order. In particular, we evaluate the static structure factor of the individual components present in the high entropy (HE) MXene experimental sample TiVCMoCr based on high-solution SEM imaging data, which suggests this HE MXene system is at least effectively multihyperuniform. We then devise a packing algorithm to generate multihyperuniform models of HE MXene systems. The MH HE MXenes are predicted to be energetically more stable compared to the prevailing (quasi)random models of the HE MXenes due to the hidden long-range order. Moreover, the MH structure exhibits a distinctly smaller lattice distortion, which has a vital effect on the electronic properties of HE MXenes, such as the density of states and charge distribution. This systematic study of HE MXenes strengthens our fundamental understanding of these systems, and suggests possible exotic physical properties, as endowed by the multihyperuniformity. MXenes ; Statistical physics ; Mathematics ; Computer science ; Physics ; Chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications
Authors: Dejan Gagoski | H. Tomas Rube | Chaitanya Rastogi | Lucas A. N. Melo | Xiaoting Li | Rashmi Voleti | Neel H. Shah | Harmen J. Bussemaker
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-23)
Field: Chemistry — Spectroscopy

Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression Short linear peptide motifs play important roles in cell signaling. They can act as modification sites for enzymes and as recognition sites for peptide binding domains. SH2 domains bind specifically to tyrosine-phosphorylated proteins, with the affinity of the interaction depending strongly on the flanking sequence. Quantifying this sequence specificity is critical for deciphering phosphotyrosine-dependent signaling networks. In recent years, protein display technologies and deep sequencing have allowed researchers to profile SH2 domain binding across thousands of candidate ligands. Here, we present a concerted experimental and computational strategy that improves the predictive power of SH2 specificity profiling. Through multi-round affinity selection and deep sequencing with large randomized phosphopeptide libraries, we produce suitable data to train an additive binding free energy model that covers the full theoretical ligand sequence space. Our models can be used to predict signaling network connectivity and the impact of missense variants in phosphoproteins on SH2 binding. Computational biology ; Phosphopeptide ; SH2 domain ; Peptide ; Biology ; Binding selectivity ; Sequence (biology) ; Sequence motif ; Biochemistry ; Tyrosine ; Tyrosine phosphorylation ; Gene Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Computational Drug Discovery Methods
Authors: Matthew Glace | Roudabeh Sadat Moazeni‐Pourasil | Daniel W. Cook | Thomas D. Roper
Source: Journal of Chemical Information and Modeling (article, 2024-06-19)
Field: Chemistry — Analytical Chemistry

Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy In this work, a new model with broad utility for quantitative spectroscopy development is reported. A primary objective of this work is to create a novel modeling procedure that may allow for higher automation of the model development process. The fundamental concept is simple yet powerful even for complex spectra and is employed with no additional preprocessing. This approach is applicable for several types of spectroscopic data to develop regression models that have similar or greater quality than the current methods. The key modeling steps are a matrix transformation and subsequent feature selection process that are collectively referred to as iterative regression of corrective baselines (IRCB). The transformed matrix (<b>X</b><sub><b>transform</b></sub>) is a linearized form of the original <b>X</b> data set. Features from <b>X<sub>t</sub></b><sub><b>ransform</b></sub> that are predictive of <b>Y</b> can be ranked and selected by ordinary least-squares regression. The best features (rows of <b>X<sub>t</sub></b><sub><b>ransform</b></sub>) are linear depictions of <b>Y</b> that can be utilized to develop regression models with several machine learning models. The IRCB workflow is first detailed by using a case study of Fourier transform infrared (FTIR) spectroscopy for prepared solutions of a three-component mixture. Next, IRCB is applied and compared to benchmark results for the 2006 "Chimiométrie" near-infrared spectroscopy (NIR) soil composition challenge and Raman measurements of a simulated nuclear waste slurry. Partial least squares regression ; Computer science ; Feature selection ; Regression ; Principal component regression ; Matrix (chemical analysis) ; Regression analysis ; Transformation (genetics) ; Biological system ; Data mining ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Le Xuan | Thanh Binh Pham | Van Chuc Nguyen | Minh Thu Nguyen | Thu Loan Nguyen | Nguyên Thuận Đào
Source: Nanomaterials (article, 2024-10-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Novel Method for Rapid and High-Performance SERS Substrate Fabrication by Combination of Cold Plasma and Laser Treatment In this paper, we report a simple yet efficient method for rapid and high-performance SERS substrate fabrication by a combination of cold plasma and laser treatment. Our analysis reveals that cold plasma pre-treatment significantly reduced surface roughness, transforming 200 nm spikes into an almost perfectly uniform surface, while enhancing the substrate’s surface energy by lowering the water contact angle from 59° to 0°, all achieved within just 30 s of 0.9-mW plasma treatment, while 15-min green-laser treatment facilitated more uniform deposition of AuNPs across the entire treated area, effectively creating the SERS substrates. The combined treatments result in enhancement of the Raman intensity (11 times) and consistency over the whole area of the SERS substrates, and their reusability (up to 10 times). The fabricated SERS substrates exhibit a significant enhancement factor of approximately 3 × 10⁸ with R6G, allowing detection down to a concentration of 10−12 M. We demonstrate the application of these SERS substrates by detecting amoxicillin—an antibiotic used worldwide to treat a diversity of bacterial infections—in a dynamic expanded linear range of seven orders (from 10−3 to 10−9 M) with high reliability (R2 = 0.98), and a detection limit of 9 × 10−10 M. Our approach to high-performance SERS substrate fabrication holds potential for further expansion to other metallic NPs like Ag, or magnetic NPs (Fe3O4). Fabrication ; Materials science ; Substrate (aquarium) ; Plasma ; Laser ; Nanotechnology ; Optoelectronics ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Nanofabrication and Lithography Techniques ; Nonlinear Optical Materials Studies
Authors: Samuel I. Mann | Zhi Lin | Sophia K. Tan | Jiaqi Zhu | Zachary X. W. Widel | Ian Bakanas | Jarrett P. Mansergh | Rui Liu | Mark J. S. Kelly | Yibing Wu | James A. Wells | Michael J. Therien | William F. DeGrado
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-10-01)
Field: Materials Science — Materials Chemistry

<i>De novo</i> design of proteins that bind naphthalenediimides, powerful photooxidants with tunable photophysical properties ABSTRACT De novo protein design provides a framework to test our understanding of protein function and to build proteins with cofactors and functions not found in nature. Here, we report the design of proteins designed to bind powerful photooxidants and the evaluation of the use of these proteins to generate diffusible small molecule reactive species for applications in proximity labeling. Because excited state dynamics are influenced by the dynamics and hydration of a photo-oxidant’s environment, it was important to not only design a binding site, but also to evaluate its dynamic properties. Thus, we used computational design in conjunction with molecular dynamics (MD) simulations to design a protein, designated NBP ( N DI B inding P rotein) that held a naphthalenediimide (NDI), a powerful photooxidant, in a programable molecular environment. Solution NMR confirmed the structure of the complex. We evaluated two NDI cofactors in this de novo protein, using ultra-fast pump-probe spectroscopy to evaluate light-triggered intra- and intermolecular electron transfer function. Moreover, we demonstrated the utility of this platform to activate multiple molecular probes for protein proximity labeling. Chemistry ; Biophysics ; Cell biology ; Computational biology ; Computer science ; Biology Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications ; Free Radicals and Antioxidants
Authors: David Moss
Source:  (preprint, 2024-04-10)
Field: Materials Science — Materials Chemistry

Integrated optical polarizers employing 2D materials Optical polarizers are essential components for the selection and manipulation of light polarization states in optical systems. Over the past decade, the rapid advancement of photonic technologies and devices has led to the development of a range of novel optical polarizers, opening avenues for many breakthroughs and expanding applications across diverse fields. Particularly, two-dimensional (2D) materials, known for their atomic thin film structures and unique optical properties, have become attractive for implementing optical polarizers with high performance and new features that were not achievable before. This paper reviews recent progress in 2D-material-based optical polarizers. First, an overview of key properties of various 2D materials for realizing optical polarizers is provided. Next, the state-of-the-art optical polarizers based on 2D materials, which are categorized into spatial-light devices, fiber devices, and integrated waveguide devices, are reviewed and compared. Finally, we discuss the current challenges of this field as well as the exciting opportunities for future technological advances. Polarizer ; Materials science ; Photonics ; Polarization (electrochemistry) ; Optoelectronics ; Optics ; Computer science ; Nanotechnology ; Engineering physics ; Birefringence ; Engineering ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; Photonic and Optical Devices
Authors: Qiuru Hua | Yiming Gu | Xianbin Guo | Chu Wu | Gang Li | Yihuang Zhang | Shudong Wang
Source: Scientia Sinica Chimica (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Tentative exploration of artificial intelligence in developing and optimizing fuel cell catalysts <p indent="0mm">With the advancement of clean energy technology, the development of fuel cell technology and catalyst technology has become a critical priority in modern technology. Artificial Intelligence (AI) is emerging as a transformative tool with its significant potential in the design and optimization of catalysts. This review explores AI’s application in fuel cell catalyst research, beginning with an overview of AI technology, including its definition, origins, tools, and applications. It then examines the use of AI for the efficient design of novel catalysts and the accurate prediction of their performance. Additionally, the paper investigates AI’s role in analyzing the reaction mechanisms of fuel cell catalysts and the interactions at reactive surface sites. The comprehensive discussion highlights AI’s contributions to the research and development of fuel cell catalysts. By constructing data-driven AI models, the paper demonstrates AI’s advantages in the rapid screening of efficient catalysts and elucidating the structure-performance relationship. This approach offers innovative methods for the preparation of efficient and controllable catalysts. The review provides new insights into fuel cell catalyst research and demonstrates AI’s preliminary applications in catalytic science. Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Jiarui Lu | Zuobai Zhang | Bozitao Zhong | Chence Shi | Jian Tang
Source: arXiv (Cornell University) (preprint, 2024-02-16)
Field: Materials Science — Materials Chemistry

Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulation data (a "zero-shot" inference). However, being agnostic of the underlying energy landscape, the accuracy of such generative model may still be limited. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner. Specifically, given a target protein of interest, we first acquire some seeding conformations from the pre-trained sampler followed by a number of physical simulations in parallel starting from these seeding samples. Then we fine-tuned the generative model using the simulation trajectories above to become a target-specific sampler. Experimental results demonstrated the superior performance of such few-shot conformation sampler at a tractable computational cost. Augment ; Sampling (signal processing) ; Artificial neural network ; Computer science ; Artificial intelligence ; Computer vision ; Philosophy Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Protein Structure and Dynamics
Authors: Sihan Zeng
Source: Highlights in Science Engineering and Technology (article, 2024-03-13)
Field: Chemistry — Analytical Chemistry

Prediction of Milk Powder Raman Spectra and Ammonium Sulfate Adulteration Concentration Based on Neural Network Model This article aims to explore the changes in Raman spectra of milk powder when different concentrations of ammonium sulfate are adulterated. By conducting neural network tests on milk powder samples with different ammonium sulfate concentrations, we studied the impact of ammonium sulfate adulteration on milk powder. The research results show that moderate concentrations of ammonium sulfate adulteration can improve the solubility and flowability of milk powder, but high concentrations of adulteration may reduce the protein content of milk powder and have negative effects on its sensory quality and safety, causing significant adverse effects on human health. Therefore, the adulteration concentration of ammonium sulfate should be strictly controlled in the production and processing of milk powder to ensure its quality and safety. Raman spectroscopy ; Ammonium sulfate ; Artificial neural network ; Ammonium ; Sulfate ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Materials science ; Organic chemistry ; Computer science ; Artificial intelligence ; Physics ; Optics Spectroscopy and Chemometric Analyses
Authors: Gulsen Taspinar | Murat Köklü
Source: Intelligent Methods in Engineering Sciences (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

Detecting Industrial Potato Chips Defects with Machine Learning Methods Using Deep Features Detection of production defects in industrial foods is vital to protect consumer health. Early detection of these errors can minimize the economic losses of manufacturers by reducing the costs of recalls and production stoppages. Additionally, continuous monitoring and improvement of product quality can increase brand reliability and customer satisfaction. Image processing can detect product defects, minimize human error and increase efficiency by performing uninterrupted inspection on the production line. Based on these reasons, this study aimed to detect potato chip errors with image processing. PepsiCo Lab Potato Chips Quality Control image dataset was used in the study. There are two classes in the dataset: defective and not defective. There are 967 images in total. SqueezeNet Convolutional Neural Network (CNN) architecture was used to extract the features of the images. With this architecture, 1000 features obtained for each image were classified with Artificial Neural Network (ANN), K Nearest Neighbor (KNN), Random Forest (RF) machine learning methods. As a result of the classifications, 0.986 classification accuracy was obtained from the ANN model, 0.927 from the KNN model, and 0.962 from the RF model. F1 Score, precision, recall and specificity metrics were used to compare the models in detail. According to the data obtained from the experimental results, it is predicted that the proposed feature extraction and classification models can detect industrial production errors occurring in potato chips. Artificial intelligence ; Computer science ; Deep learning ; Machine learning Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Changchun He | Shao-Gang Xu | Yu‐Jun Zhao | Hu Xu | Xiao‐Bao Yang
Source: arXiv (Cornell University) (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

Entropy-driven electron density and effective model Hamiltonian for boron systems The unique electron deficiency of boron makes it challenging to determine the stable structures, leading to a wide variety of forms. In this work, we introduce a statistical model based on grand canonical ensemble theory that incorporates the octet rule to determine electron density in boron systems. This parameter-free model, referred to as the bonding free energy (BFE) model, aligns well with first-principles calculations and accurately predicts total energies. For borane clusters, the model successfully predicts isomer energies, hydrogen diffusion pathways, and optimal charge quantity for closo-boranes. In all-boron clusters, the absence of B-H bond constraints enables increased electron delocalization and flexibility. The BFE model systematically explains the geometric structures and chemical bonding in boron clusters, revealing variations in electron density that clarify their structural diversity. For borophene, the BFE model predicts that hexagonal vacancy distributions are influenced by bonding entropy, with uniform electron density enhancing stability. Notably, our model predicts borophenes with a vacancy concentration of 1 6 to exhibit increased stability with long-range periodicity. Therefore, the BFE model serves as a practical criterion for structure prediction, providing essential insights into the stability and physical properties of boron-based systems. Boron ; Entropy (arrow of time) ; Statistical physics ; Hamiltonian (control theory) ; Electron ; Physics ; Mathematics ; Thermodynamics ; Quantum mechanics ; Mathematical optimization ; Nuclear physics Boron and Carbon Nanomaterials Research ; Inorganic Chemistry and Materials ; Machine Learning in Materials Science
Authors: Suzuki Masato | Igarashi Yasuhiko
Source: Institutional Repositories DataBase (IRDB) (article, 2024-11-18)
Field: Materials Science — Polymers and Plastics

Application of a Hessian-Based Image-Processing Method for Enhanced Visualization of Nanoscale Rubber Cross-Linked Network Structures from Electron Microscopy Images Characterizing rubber cross-linking, nanoscale structures that significantly influence rubber's mechanical properties, is crucial for advancing the design of rubber materials, and electron microscopy is essential for observing the cross-linked networks. However, reliance on manual markup to make the network visible introduces subjectivity and hinders the objective analysis of many samples. This study presents an image-processing method based on the Hessian matrix to enhance and extract cross-linking network structures. We confirmed that this method effectively enhanced network structures across a wide range of cross-linking levels. From parameters extracted from enhanced images, we derived an equation to predict the cross-link density and mechanical properties. This equation outperformed the use of image luminance alone, thus confirming the effectiveness of the image enhancement method in characterizing cross-linking. We successfully estimated nanoscale cross-linked network structure parameters directly from images previously measured indirectly. This versatile approach, applicable to various structures, is expected to significantly improve nanoscale structural analysis, thereby paving the way for the development of higher-performance materials. Nanoscopic scale ; Natural rubber ; Visualization ; Luminance ; Network structure ; Hessian matrix ; Silicone rubber Polymer Nanocomposites and Properties ; Elasticity and Material Modeling ; Lubricants and Their Additives
Authors: Andreza Menezes Lima | Kelly Cristine da Silveira | Antonio Carlos Dias | Alexandre Antunes Ribeiro | Antônio José da Silva Neto
Source:  (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

APLICAÇÃO DE APRENDIZADO DE MÁQUINA NA PREDIÇÃO DE VISCOSIDADE DE PASTAS CERÂMICAS PARA MANUFATURA ADITIVA Anais do Encontro Nacional de Modelagem Computacional e Encontro de Ciência e Tecnologia de Materiais (978-65-272-1055-9) - Aplicação De Aprendizado De Máquina Na Predição De Viscosidade De Pastas Cerâmicas Para Manufatura Aditiva Humanities ; Physics ; Art Pigment Synthesis and Properties
Authors: Jin Kousaka | Atsuko H. Iwane | Yuichi Togashi
Source: Biophysical Journal (article, 2024-02-01)
Field: Materials Science — Surfaces, Coatings and Films

Visualizing the dynamics of cell division through 3D models constructed from electron microscopy Division (mathematics) ; Dynamics (music) ; Electron microscope ; Nanotechnology ; Materials science ; Physics ; Optics ; Mathematics Electron and X-Ray Spectroscopy Techniques