jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyue Yang | Pingping Zeng | Jin Wen | Chuanlin Wang | Liangyuan Yao | Min He
Source: Chinese Herbal Medicines (article, 2023-11-14)
Field: Chemistry — Spectroscopy

Gain deeper insights into traditional Chinese medicines using multidimensional chromatography combined with chemometric approaches Traditional Chinese medicines (TCMs) possess a rich historical background, unique theoretical framework, remarkable therapeutic efficacy, and abundant resources. However, the modernization and internationalization of TCMs have faced significant obstacles due to their diverse ingredients and unknown mechanisms. To gain deeper insights into the phytochemicals and ensure the quality control of TCMs, there is an urgent need to enhance analytical techniques. Currently, two-dimensional (2D) chromatography, which incorporates two independent separation mechanisms, demonstrates superior separation capabilities compared to the traditional one-dimensional (1D) separation system when analyzing TCMs samples. Over the past decade, new techniques have been continuously developed to gain actionable insights from complex samples. This review presents the recent advancements in the application of multidimensional chromatography for the quality evaluation of TCMs, encompassing 2D-gas chromatography (GC), 2D-liquid chromatography (LC), as well as emerging three-dimensional (3D)-GC, 3D-LC, and their associated data-processing approaches. These studies highlight the promising potential of multidimensional chromatographic separation for future phytochemical analysis. Nevertheless, the increased separation capability has resulted in higher-order data sets and greater demands for data-processing tools. Considering that multidimensional chromatography is still a relatively nascent research field, further hardware enhancements and the implementation of chemometric methods are necessary to foster its robust development. Computer science ; Biochemical engineering ; Chromatography ; Chemistry ; Engineering Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Natural product bioactivities and synthesis
Authors: Stephanie A. Wankowicz | Ashraya Ravikumar | Shivani Sharma | Blake T. Riley | Akshay Raju | Jessica Flowers | Daniel W. Hogan | Henry van den Bedem | D.A. Keedy | James S. Fraser
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-28)
Field: Materials Science — Materials Chemistry

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mi>f</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:math> and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function. Crystallography ; X-ray crystallography ; Computer science ; Physics ; Diffraction ; Chemistry ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Yue Zhang | Yuan Li | Cong Zhou | Zhou Jun-hui | Tiegui Nan | Jian Yang | Luqi Huang
Source: Journal of Food Quality (article, 2023-02-22)
Field: Chemistry — Analytical Chemistry

Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method Tiegun yam is a typical food and medicine agricultural product, which has the effects of nourishing the kidney and benefitting the lungs. The quality and price of Tiegun yam are affected by its origin, and counterfeiting and adulteration are common. Therefore, it is necessary to establish a method to identify the origin and index component contents of Tiegun yam. Hyperspectral imaging combined with chemometrics was used, for the first time, to explore and implement the identification of origin and index component contents of Tiegun yam. The origin identification models were established by partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF) using full wavelength and feature wavelength. Compared with other models, MSC-PLS-DA is the best model, and the accuracy of the training set and prediction set is 100% and 98.40%. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) models were used to predict the contents of starch, polysaccharide, and protein in Tiegun yam powder. The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. The characteristic wavelength extracted by the successive projections algorithm (SPA) method can achieve similar results as the full-wavelength model. These results confirmed the application of hyperspectral imaging (HSI) in the identification of the origin and the rapid nondestructive prediction of starch, polysaccharide, and protein contents of Tiegun yam powder. Therefore, the HSI combined with the chemometric method was available for conveniently and accurately determining the origin and index component contents of Tiegun yam, which can expect to be an attractive alternative method for identifying the origin of other food. Hyperspectral imaging ; Partial least squares regression ; Chemometrics ; Principal component analysis ; Support vector machine ; Random forest ; Artificial intelligence ; Pattern recognition (psychology) ; Linear discriminant analysis ; Mathematics ; Biological system ; Computer science ; Statistics ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Changhai Zhang | Bowen Zhang | Chao Yin | Chao Tang | Tiandong Zhang | Xianli Liu | Qingguo Chi
Source: Journal of the American Ceramic Society (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Achieving high‐energy storage performance of PbZro <sub>3</sub> ‐based thin films utilizing insulation interlayer and low‐temperature annealing Abstract PbZrO 3 (PZO)‐based antiferroelectric thin films are of great interest due to their high‐power density and fast charging and discharging capability. However, the problems of low breakdown strength and inferior energy storage density of PZO films have not been well solved. In this work, the insulating MgO as the blocking interlayer is inserted into PbZrO 3 films (abbreviated as P/M/P), which can inhibit the electric charge transfer and enhance the breakdown strength, as well as regulation of the polarization behavior. The results show that the maximal endurable electric field is significantly improved, and the double‐hysteresis characteristic disappeared after introducing MgO blocking interlayer. The energy storage density of P/M/P films reaches 21.97 J/cm 3 under 1700 kV/cm, accompanying an ultralow efficiency of 44.01% due to the severe polarization loss. Furthermore, low‐temperature annealing is performed to suppress the polarization loss, and an energy storage density of 17.27 J/cm 3 accompanying a high efficiency of 75.53% is obtained at 3100 kV/cm, still exhibiting good stability after 1 × 10 7 fatigue cycles. This study demonstrates that combining the insulating interlayer and the low‐temperature annealing endow the PZO‐based films significantly improved energy storage properties, having great potential to be used in the dielectric capacitors. Annealing (glass) ; Materials science ; Dielectric ; Energy storage ; Thin film ; Electric field ; Capacitor ; Polarization (electrochemistry) ; Dielectric loss ; Composite material ; Optoelectronics ; Voltage ; Electrical engineering ; Nanotechnology ; Chemistry ; Thermodynamics ; Power (physics) Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Dielectric materials and actuators
Authors: Fei Teng | Yang Wang | Ming Liu | Shuyun Tian | Goran Stjepanović | Ming-Yuan Su
Source: Structure (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Cryo-EM structure of the KLHL22 E3 ligase bound to an oligomeric metabolic enzyme Ubiquitin ligase ; DNA ligase ; Chemistry ; Ubiquitin-Protein Ligases ; Enzyme ; Biochemistry ; Protein structure ; Cell biology ; Biophysics ; Biology ; Ubiquitin ; Gene Enzyme Structure and Function ; RNA modifications and cancer ; Biochemical and Molecular Research
Authors: Soroush Najafi | Alireza Sheikhani | M. Sabbaghian | Péter Nagy | Klaudia Fekete | Jenõ Gubicza
Source: Materials (article, 2023-04-02)
Field: Materials Science — Biomaterials

Modification of the Tensile Performance of an Extruded ZK60 Magnesium Alloy with the Addition of Rare Earth Elements The influence of rare earth (RE) elements on the microstructure and mechanical performance of an extruded ZK60 Mg alloy was studied. Two types of RE elements were added to a ZK60 material and then extruded at a ratio of 18:1. The first new alloy contained 2 wt% Y while the second one was produced using 2 wt% Ce-rich mischmetal. The microstructure, the texture, and the dislocation density in a base ZK60 alloy and two materials with RE additives were studied by scanning electron microscopy, electron backscattered diffraction, and X-ray line profile analysis, respectively. It was found that the addition of RE elements caused a finer grain size, the formation of new precipitates, and changes in the initial fiber texture. As a consequence, Y and Ce-rich RE elements increased the strength and reduced the ductility. The addition of these two types of RE elements to the ZK60 alloy decreased the work hardening capacity and the hardening exponent mainly due to grain refinement. Mischmetal ; Materials science ; Microstructure ; Alloy ; Ultimate tensile strength ; Scanning electron microscope ; Ductility (Earth science) ; Texture (cosmology) ; Grain size ; Metallurgy ; Rare earth ; Magnesium ; Composite material ; Hydrogen storage ; Creep Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials
Authors: Xinyao Xu | Wenlin Zhao | Liquan Wang | Jiaping Lin | Lei Du
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Efficient exploration of compositional space for high-performance copolymers <i>via</i> Bayesian optimization The traditional approach employed in copolymer compositional design, which relies on trial-and-error, faces low-efficiency and high-cost obstacles when attempting to simultaneously improve multiple conflicting properties. For example, designing co-cured polycyanurates that exhibit both moisture and thermal resistance, along with high modulus, is a long-term challenge because of the intrinsic trade-offs between these properties. In this work, to surmount these barriers, we developed a Bayesian optimization (BO)-guided method to expedite the discovery of co-cured polycyanurates exhibiting low water uptake, coupled with higher glass transition temperature and Young's modulus. By virtue of the knowledge of molecular simulations, benchmarking studies were carried out to develop an effective BO-guided method. Propelled by the developed method, several copolymers with improved comprehensive properties were obtained experimentally in a few iterations. This work provides guidance for efficiently designing other high-performance copolymers. Bayesian optimization ; Copolymer ; Bayesian probability ; Space (punctuation) ; Computer science ; Chemical space ; Mathematical optimization ; Materials science ; Artificial intelligence ; Mathematics ; Chemistry ; Polymer Polymer Nanocomposites and Properties ; Conducting polymers and applications ; Machine Learning in Materials Science
Authors: Selim Romero | Tunna Baruah | Rajendra R. Zope
Source: The Journal of Chemical Physics (article, 2023-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-state gaps and self-interaction-corrected density functional approximations: Octahedral Fe(II) complexes as case study Accurate prediction of a spin-state energy difference is crucial for understanding the spin crossover phenomena and is very challenging for density functional approximations, especially for local and semi-local approximations due to delocalization errors. Here, we investigate the effect of the self-interaction error removal from the local spin density approximation (LSDA) and Perdew–Burke–Ernzerhof generalized gradient approximation on the spin-state gaps of Fe(II) complexes with various ligands using recently developed locally scaled self-interaction correction (LSIC) by Zope et al. [J. Chem. Phys. 151, 214108 (2019)]. The LSIC method is exact for one-electron density, recovers the uniform electron gas limit of the underlying functional, and approaches the well-known Perdew–Zunger self-interaction correction (PZSIC) as a particular case when the scaling factor is set to unity. Our results, when compared with reference diffusion Monte Carlo results, show that the PZSIC method significantly overestimates spin-state gaps favoring low spin states for all ligands and does not improve upon density functional approximations. The perturbative LSIC-LSDA using PZSIC densities significantly improves the gaps with a mean absolute error of 0.51 eV but slightly overcorrects for the stronger CO ligands. The quasi-self-consistent LSIC-LSDA, such as coupled-cluster single double and perturbative triple [CCSD(T)], gives a correct sign of spin-state gaps for all ligands with a mean absolute error of 0.56 eV, comparable to that of CCSD(T) (0.49 eV). Delocalized electron ; Spin (aerodynamics) ; Chemistry ; Density functional theory ; Physics ; Condensed matter physics ; Electron ; Quantum mechanics ; Thermodynamics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies
Authors: Luhuan Wu | Brian L. Trippe | Christian A. Naesseth | David M. Blei | John P. Cunningham
Source: UvA-DARE (University of Amsterdam) (preprint, 2023-06-30)
Field: Materials Science — Materials Chemistry

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily depended on task-specific conditional training or error-prone heuristic approximations. Ideally, a conditional generation method should provide exact samples for a broad range of conditional distributions without requiring task-specific training. To this end, we introduce the Twisted Diffusion Sampler, or TDS. TDS is a sequential Monte Carlo (SMC) algorithm that targets the conditional distributions of diffusion models through simulating a set of weighted particles. The main idea is to use twisting, an SMC technique that enjoys good computational efficiency, to incorporate heuristic approximations without compromising asymptotic exactness. We first find in simulation and in conditional image generation tasks that TDS provides a computational statistical trade-off, yielding more accurate approximations with many particles but with empirical improvements over heuristics with as few as two particles. We then turn to motif-scaffolding, a core task in protein design, using a TDS extension to Riemannian diffusion models. On benchmark test cases, TDS allows flexible conditioning criteria and often outperforms the state of the art. Heuristics ; MNIST database ; Computer science ; Range (aeronautics) ; USable ; Heuristic ; Mathematical optimization ; Algorithm ; Benchmark (surveying) ; Sampling (signal processing) ; Applied mathematics ; Artificial intelligence ; Mathematics ; Deep learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Model Reduction and Neural Networks
Authors: Nhat-Quang Dang | Trong-Tai Ho | Tuyet-Doan Vo-Nguyen | Young-Woo Youn | Hyeon-Soo Choi | Yong‐Hwa Kim
Source: Energies (article, 2023-12-19)
Field: Materials Science — Materials Chemistry

Supervised Contrastive Learning for Fault Diagnosis Based on Phase-Resolved Partial Discharge in Gas-Insulated Switchgear Supervised contrastive learning (SCL) has recently emerged as an alternative to conventional machine learning and deep neural networks. In this study, we propose an SCL model with data augmentation techniques using phase-resolved partial discharge (PRPD) in gas-insulated switchgear (GIS). To increase the fault data for training, we employ Gaussian noise adding, Gaussian noise scaling, random cropping, and phase shifting for supervised contrastive loss. The performance of the proposed SCL was verified by four types of faults in the GIS and on-site noise using an on-line ultra-high-frequency (UHF) partial discharge (PD) monitoring system. The experimental results show that the proposed SCL achieves a classification accuracy of 97.28% and outperforms the other algorithms, including support vector machines (SVM), multilayer perceptron (MLP), and convolution neural networks (CNNs) in terms of classification accuracy, by 6.8%, 4.28%, 2.04%, respectively. Switchgear ; Partial discharge ; Support vector machine ; Computer science ; Pattern recognition (psychology) ; Noise (video) ; Random forest ; Fault (geology) ; Artificial intelligence ; Perceptron ; Artificial neural network ; Convolutional neural network ; Supervised learning ; Gaussian noise ; Convolution (computer science) ; Machine learning ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Infrastructure Maintenance and Monitoring
Authors: Xiaoqiang Zhang | Danan Yang | Min Xu | Aaron B. Naden | Mayken Espinoza‐Andaluz | Tingshuai Li | John T. S. Irvine | Martin Andersson
Source: Journal of The Electrochemical Society (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Analysing Tortuosity for Solid Oxide Fuel Cell Anode Material: Experiments and Modeling Solid oxide fuel cells (SOFCs) directly convert chemical energy to electricity with high electrical efficiency. It involves gas transport through the porous electrode to the three-phase boundaries (TPB). The tortuosity of gas transport relates the bulk diffusion of gas in free space to the effective diffusion coefficient of gas migrating through a porous material. Therefore, determining the tortuosity is of great importance. This paper tests button SOFCs with NiO-YSZ as anode material followed by dual beam-focused ion beam scanning electron microscopy (FIB-SEM) to obtain 2D serial slice images. Based on processed 2D images and reconstructed 3D microstructure, the tortuosity is calculated using three approaches i.e., porosity-tortuosity correlations, voxel-based, and path-length-based approaches. The test results show that a decrease in Ni content in the anode greatly decreases the cell performance due to a decreased percolated electronic phase. The sample with low performance has high tortuosity. Different approaches vary regarding the tortuosity value and computational time. The path-length-based approach can achieve reasonable accuracy in a relatively short time but is only valid for using the longest path length. Tortuosity ; Anode ; Materials science ; Porosity ; Focused ion beam ; Diffusion ; Oxide ; Triple phase boundary ; Scanning electron microscope ; Solid oxide fuel cell ; Gaseous diffusion ; Microstructure ; Electrode ; Analytical Chemistry (journal) ; Composite material ; Ion ; Chemistry ; Metallurgy ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Fuel Cells and Related Materials
Authors: Jiangsheng Gui | Huirong Xu | Jingyi Fei
Source: Sensors (article, 2023-01-06)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Soybean Pest Based on Hyperspectral Image and Attention-ResNet Meta-Learning Model Soybean plays an important role in food, medicine, and industry. The quality inspection of soybean is essential for soybean yield and the agricultural economy. However, soybean pest is an important factor that seriously affects soybean yield, among which leguminivora glycinivorella matsumura is the most frequent pest. Aiming at the problem that the traditional detection methods have low accuracy and need a large number of samples to train the model, this paper proposed a detection method for leguminivora glycinivorella matsumura based on an A-ResNet (Attention-ResNet) meta-learning model. In this model, the ResNet network was combined with Attention to obtain the feature vectors that can better express the samples, so as to improve the performance of the model. As well, the classifier was designed as a multi-class support vector machine (SVM) to reduce over-fitting. Furthermore, in order to improve the training stability of the model and the prediction performance on the testing set, the traditional Batch Normalization was replaced by the Layer Normalization, and the Label Smooth method was used to punish the original loss. The experimental results showed that the accuracy of the A-ResNet meta-learning model reached 94.57 ± 0.19%, which can realize rapid and accurate nondestructive detection, and provides theoretical support for the intelligent detection of soybean pests. Support vector machine ; Normalization (sociology) ; Artificial intelligence ; Hyperspectral imaging ; Computer science ; Classifier (UML) ; Machine learning ; Pattern recognition (psychology) ; PEST analysis Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies