jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaihua Wei | Bojian Chen | Zejian Li | Dongmei Chen | Guangyu Liu | Hongze Lin | Baihua Zhang
Source: Sensors (article, 2022-10-13)
Field: Chemistry — Analytical Chemistry

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life. Fluorescence ; Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Fluorescence-lifetime imaging microscopy ; Remote sensing ; Computer vision ; Optics ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Tea Polyphenols and Effects
Authors: Clara Vazquez‐Martel | Philipp Mainik | Eva Blasco
Source: Organic Materials (article, 2022-11-09)
Field: Chemistry — Organic Chemistry

Natural and Naturally Derived Photoinitiating Systems for Light-Based 3D Printing Photoinitiating systems (PISs) are key components in photocurable formulations (inks) for three-dimensional (3D) printing. Natural PISs are promising and more environmentally friendly alternatives to conventional photoinitiators. These systems offer enhanced features such as less toxicity and therefore, higher biocompatibility. Furthermore, they can be found in a large “color palette” variety (absorbing in the different regions of the UV-visible spectrum) enabling new possibilities in the field of 3D printing. In this review, we outline the current efforts to implement natural and naturally derived PISs in light-based 3D printing. First, recently applied natural PISs and their performance for light-based 3D printing are discussed. In a second part, an overview of naturally derived PISs including the most common synthetic strategies are provided. Last, current challenges as well as future perspectives of the field are discussed. Introduction Natural Photoinitiating Systems Naturally Derived Photoinitiating Systems Conclusions and Outlook Palette (painting) ; 3D printing ; Biochemical engineering ; Nanotechnology ; Process engineering ; Materials science ; Computer science ; Natural (archaeology) ; Environmentally friendly ; Engineering ; Composite material Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Dental materials and restorations
Authors: Daniel Barter | Evan Walter Clark Spotte‐Smith | Nikita S. Redkar | Aniruddh Khanwale | Shyam Dwaraknath | Kristin A. Persson | Samuel M. Blau
Source: Digital Discovery (article, 2022-11-30)
Field: Chemistry — Electrochemistry

Predictive stochastic analysis of massive filter-based electrochemical reaction networks New methods for chemical reaction network construction and analysis enable the discovery of novel products and mechanisms in battery electrochemistry. Electrochemistry ; Computer science ; Chemistry ; Electrode Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Shahla Khalili | Nazanin Ghane | Saied Nouri Khorasani | Fariba Heydari | Arjan Atwal | Pooya Davoodi
Source: Polymers (article, 2022-06-24)
Field: Materials Science — Biomaterials

Cytocompatibility and Antibacterial Properties of Coaxial Electrospun Nanofibers Containing Ciprofloxacin and Indomethacin Drugs A coaxial nanofibrous scaffold of poly (ε-caprolactone) and gelatin/cellulose acetate encapsulating anti-inflammatory and antibacterial drugs was co-electrospun for skin tissue regeneration. Indomethacin and ciprofloxacin as model drugs were added to the core and the shell solutions, respectively. The effect of the drugs’ presence and crosslinking on the scaffold properties was investigated. TEM images confirmed the core−shell structure of the scaffold. The fiber diameter and the pore size of the scaffold increased after crosslinking. The tensile properties of the scaffold improved after crosslinking. The crosslinked scaffold illustrated a higher rate of swelling, and a lower rate of degradation and drug release compared to the uncrosslinked one. Fitting the release data into the Peppas equation showed that Fickian diffusion was the dominant mechanism of drug release from the scaffolds. The results of biocompatibility evaluations showed no cytotoxicity and suitable adhesion and cell growth on the prepared core−shell structure. The antibacterial activity of the scaffolds was studied against one of the most common pathogens in skin wounds, where the existence of ciprofloxacin could prevent the growth of the Staphylococcus aureus bacteria around the scaffold. The obtained results suggested a new coaxial nanofibrous scaffold as a promising candidate for simultaneous tissue regeneration and controlled drug release. Scaffold ; Biocompatibility ; Nanofiber ; Materials science ; Gelatin ; Antibacterial activity ; Electrospinning ; Ultimate tensile strength ; Biomedical engineering ; Chemistry ; Chemical engineering ; Composite material ; Bacteria ; Organic chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Silk-based biomaterials and applications
Authors: Antonio Alex‐Amor | Ángel Palomares‐Caballero | Carlos Molero
Source: Electronics (article, 2022-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3-D Metamaterials: Trends on Applied Designs, Computational Methods and Fabrication Techniques Metamaterials are artificially engineered devices that go beyond the properties of conventional materials in nature. Metamaterials allow for the creation of negative refractive indexes; light trapping with epsilon-near-zero compounds; bandgap selection; superconductivity phenomena; non-Hermitian responses; and more generally, manipulation of the propagation of electromagnetic and acoustic waves. In the past, low computational resources and the lack of proper manufacturing techniques have limited attention towards 1-D and 2-D metamaterials. However, the true potential of metamaterials is ultimately reached in 3-D configurations, when the degrees of freedom associated with the propagating direction are fully exploited in design. This is expected to lead to a new era in the field of metamaterials, from which future high-speed and low-latency communication networks can benefit. Here, a comprehensive overview of the past, present, and future trends related to 3-D metamaterial devices is presented, focusing on efficient computational methods, innovative designs, and functional manufacturing techniques. Metamaterial ; Fabrication ; Computer science ; Materials science ; Optoelectronics ; Medicine Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Chen-Bo-Wen LI | Zhao Li | Juan Wang | Yi-Xuan LIU | Jing-Tong LU | Ze Xu | Pak‐Sheng Soon | Ke Bi | Chuan CHEN | Ke Wang
Source: Journal of Advanced Ceramics (article, 2022-12-08)
Field: Materials Science — Materials Chemistry

Sodium lithium niobate lead-free piezoceramics for high-power applications: Fundamental, progress, and perspective With the capability of interconversion between electrical and mechanical energy, piezoelectric materials have been revolutionized by the implementation of perovskite-piezoelectric-ceramic-based studies over 70 years. In particular, the market of piezoelectric ceramics has been dominated by lead zirconate titanate for decades. Nowadays, the research on piezoelectric ceramics is largely driven by cutting-edge technological demand as well as the consideration of a sustainable society. Hence, environmental-friendly lead-free piezoelectric materials have emerged to replace lead-based Pb(Zr,Ti)O<sub>3</sub> (PZT) compositions. Owing to the inherent high mechanical quality factor (<i>Q</i><sub>m</sub>) and low energy loss, (Li,Na)NbO<sub>3</sub> (LNN) materials have recently drawn increasing attention and brought advantages to high-power piezoelectric applications. Although the crystallographic structures of LNN materials were intensively investigated for decades, the technical strategies for electrical performance are still limited. As a result, the property enhancement appears to have approached a plateau. This review traces the progress in the development of LNN materials, starting from the polymorphism in terms of the crystal structures, phase transitions, and local structural distortions. Then, the key milestone works on the functional tunability of LNN are reviewed with emphasis on involved engineering approaches. The exceptional performance at a large vibration velocity makes LNN ceramics promising for high-power applications, such as ultrasonic welding (UW) and ultrasonic osteotomes (UOs). The remaining challenges and some strategic insights for synergistically engineering the functional performance of LNN piezoceramics are also suggested. Piezoelectricity ; Materials science ; Structural material ; Ceramic ; Lead zirconate titanate ; Engineering physics ; Nanotechnology ; Mechanical engineering ; Ferroelectricity ; Composite material ; Optoelectronics ; Engineering Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Wen‐Sheng Zhao | Rui Zhang | Da‐Wei Wang
Source: Micromachines (review, 2022-05-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress in Physics-Based Modeling of Electromigration in Integrated Circuit Interconnects The advance of semiconductor technology not only enables integrated circuits with higher density and better performance but also increases their vulnerability to various aging mechanisms which occur from front-end to back-end. Analysis on the impact of aging mechanisms on circuits' reliability is crucial for the design of reliable and sustainable electronic systems at advanced technology nodes. As one of the most crucial back-end aging mechanisms, electromigration deserves research efforts. This paper introduces recent studies on physics-based modeling of electromigration aging of on-chip interconnects. At first, the background of electromigration is introduced. The conventional method and physics-based modeling for electromigration are described. Then studies on how electromigration affects powers grids and signal interconnects are discussed in detail. Some of them focus on the comprehensiveness of modeling methodology, while others aim at the strategies for improving computation accuracy and speed and the strategies for accelerating/decelerating aging. Considering the importance of electromigration for circuit reliability, this paper is dedicated to providing a review on physics-based modeling methodologies on electromigration and their applications for integrated circuits interconnects. Electromigration ; Reliability (semiconductor) ; Integrated circuit ; Electronic circuit ; Electronic engineering ; Computer science ; Reliability engineering ; Engineering physics ; Electrical engineering ; Engineering ; Physics Copper Interconnects and Reliability ; Semiconductor materials and devices ; Electronic Packaging and Soldering Technologies
Authors: Kunshan Yao | Jun Sun | Jiehong Cheng | Min Xu | Chen Chen | Xin Zhou | Chunxia Dai
Source: Foods (article, 2022-07-08)
Field: Chemistry — Analytical Chemistry

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401-1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R<sup>2</sup><sub>P</sub>) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs. Hyperspectral imaging ; Ovalbumin ; Content (measure theory) ; Biological system ; Chemistry ; Computer science ; Biology ; Artificial intelligence ; Mathematics ; Immunology ; Immune system Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Francis J. O’Reilly | Andrea Graziadei | Christian Forbrig | Rica Bremenkamp | Kristine Charles | Swantje Lenz | Christoph Elfmann | Lutz Fischer | Jörg Stülke | Juri Rappsilber
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-26)
Field: Chemistry — Spectroscopy

Protein complexes in cells by AI-assisted structural proteomics Summary Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enables a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry, cofractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis . We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the Subti Wiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interface, and is applicable to genetically intractable organisms, including pathogenic bacteria. Lysis ; Computational biology ; Proteomics ; Protein–protein interaction ; Ribosome ; Bacillus subtilis ; Function (biology) ; Structural biology ; Mass spectrometry ; Biology ; Bacterial protein ; Chemistry ; Cell biology ; Bacteria ; Biochemistry ; RNA ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Bioinformatics and Genomic Networks
Authors: Haojie Huang | Dian Wang | Robin Walters | Robert Platt
Source:  (article, 2022-06-27)
Field: Materials Science — Materials Chemistry

Equivariant Transporter Network Transporter Net is a recently proposed framework for pick and place that is able to learn good manipulation policies from a very few expert demonstrations A key reason why Transporter Net is so sample efficient is that the model incorporates rotational equivariance into the pick-conditioned place module, i.e. the model immediately generalizes learned pick-place knowledge to objects presented in different pick orientations. This paper proposes a novel version of Transporter Net that is equivariant to both pick and place orientation. As a result, our model immediately generalizes pick-place knowledge to different place orientations in addition to generalizing the pick orientation as before. Ultimately, our new model is more sample efficient and achieves better pick and place success rates than the baseline Transporter Net model. Computer science ; Equivariant map ; Computer network ; Mathematics ; Pure mathematics Lanthanide and Transition Metal Complexes ; Markov Chains and Monte Carlo Methods ; Graph theory and applications
Authors: Maximilian E. Merkel | Alberto Carta | Sophie Beck | Alexander Hampel
Source: The Journal of Open Source Software (article, 2022-09-23)
Field: Materials Science — Materials Chemistry

solid_dmft: gray-boxing DFT+DMFT materials simulationswith TRIQS Strongly correlated systems are a class of materials whose electronic structure is heavily influenced by the effect of electron-electron interactions. In these systems, an effective singleparticle description may not capture the many-body effects accurately. Although density functional theory (DFT) plus dynamical mean-field theory (DMFT) has proven successful in describing strongly correlated electron systems for over two decades, only very recently ready-to-use software packages have begun to become available, with most scientific research carried out by in-house codes developed and used in individual research groups. Given the complexity of the method, there is also the question of whether users should implement the formalism themselves for each problem or whether black-box software, analogous to popular DFT packages, would be beneficial to the community. Gray (unit) ; Materials science ; Condensed matter physics ; Physics ; Medicine Machine Learning in Materials Science ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Kanika Ghildiyal | Manjit Panigrahi | Harshit Kumar | Divya Rajawat | Sonali Sonejita Nayak | Chuzhao Lei | Bharat Bhushan | Triveni Dutt
Source: Animal Genetics (review, 2022-11-09)
Field: Materials Science — Biomaterials

Selection signatures for fiber production in commercial species: A review Natural fibers derived from diverse animal species have gained increased attention in recent years due to their favorable environmental effects, long-term sustainability benefits, and remarkable physical and mechanical properties that make them valuable raw materials used for textile and non-textile production. Domestication and selective breeding for the economically significant fiber traits play an imperative role in shaping the genomes and, thus, positively impact the overall productivity of the various fiber-producing species. These selection pressures leave unique footprints on the genome due to alteration in the allelic frequencies at specific loci, characterizing selective sweeps. Recent advances in genomics have enabled the discovery of selection signatures across the genome using a variety of methods. The increased demand for 'green products' manufactured from natural fibers necessitates a detailed investigation of the genomes of the various fiber-producing plant and animal species to identify the candidate genes associated with important fiber attributes such as fiber diameter/fineness, color, length, and strength, among others. The objective of this review is to present a comprehensive overview of the concept of selection signature and selective sweeps, discuss the main methods used for its detection, and address the selection signature studies conducted so far in the diverse fiber-producing animal species. Biology ; Domestication ; Selection (genetic algorithm) ; Genome ; Biotechnology ; Fiber ; Genomics ; Animal species ; Evolutionary biology ; Genetics ; Gene ; Computer science ; Materials science Silk-based biomaterials and applications ; Animal Genetics and Reproduction ; Research in Cotton Cultivation
Authors: M. Eric Irrgang | Caroline Davis | Peter M. Kasson
Source: PLoS Computational Biology (article, 2022-02-14)
Field: Chemistry — Spectroscopy

gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support Gmxapi provides an integrated, native Python API for both standard and advanced molecular dynamics simulations in GROMACS. The Python interface permits multiple levels of integration with the core GROMACS libraries, and legacy support is provided via an interface that mimics the command-line syntax, so that all GROMACS commands are fully available. Gmxapi has been officially supported since the GROMACS 2019 release and is enabled by default in current versions of the software. Here we describe gmxapi 0.3 and later. Beyond simply wrapping GROMACS library operations, the API permits several advanced operations that are not feasible using the prior command-line interface. First, the API allows custom user plugin code within the molecular dynamics force calculations, so users can execute custom algorithms without modifying the GROMACS source. Second, the Python interface allows tasks to be dynamically defined, so high-level algorithms for molecular dynamics simulation and analysis can be coordinated with loop and conditional operations. Gmxapi makes GROMACS more accessible to custom Python scripting while also providing support for high-level data-flow simulation algorithms that were previously feasible only in external packages. Python (programming language) ; Computer science ; Plug-in ; Scripting language ; Programming language ; Software ; Interface (matter) ; Computational science ; Molecular dynamics ; Operating system ; Parallel computing ; Chemistry Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Protein Structure and Dynamics