jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Katja‐Sophia Csizi | Markus Reiher
Source: Journal of Computational Chemistry (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Automated preparation of nanoscopic structures: Graph‐based sequence analysis, mismatch detection, and pH‐consistent protonation with uncertainty estimates Structure and function in nanoscale atomistic assemblies are tightly coupled, and every atom with its specific position and even every electron will have a decisive effect on the electronic structure, and hence, on the molecular properties. Molecular simulations of nanoscopic atomistic structures therefore require accurately resolved three-dimensional input structures. If extracted from experiment, these structures often suffer from severe uncertainties, of which the lack of information on hydrogen atoms is a prominent example. Hence, experimental structures require careful review and curation, which is a time-consuming and error-prone process. Here, we present a fast and robust protocol for the automated structure analysis and pH-consistent protonation, in short, ASAP. For biomolecules as a target, the ASAP protocol integrates sequence analysis and error assessment of a given input structure. ASAP allows for p <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub><mml:mrow><mml:mi>K</mml:mi></mml:mrow> <mml:mrow><mml:mtext>a</mml:mtext></mml:mrow> </mml:msub> </mml:mrow> </mml:math> prediction from reference data through Gaussian process regression including uncertainty estimation and connects to system-focused atomistic modeling described in Brunken and Reiher (J. Chem. Theory Comput. 16, 2020, 1646). Although focused on biomolecules, ASAP can be extended to other nanoscopic objects, because most of its design elements rely on a general graph-based foundation guaranteeing transferability. The modular character of the underlying pipeline supports different degrees of automation, which allows for (i) efficient feedback loops for human-machine interaction with a low entrance barrier and for (ii) integration into autonomous procedures such as automated force field parametrizations. This facilitates fast switching of the pH-state through on-the-fly system-focused reparametrization during a molecular simulation at virtually no extra computational cost. Computer science ; Molecular dynamics ; Modular design ; Nanoscopic scale ; Algorithm ; Theoretical computer science ; Chemistry ; Nanotechnology ; Computational chemistry ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Nafis Khuriyati | Anggoro Cahyo Sukartiko | Mohammad Affan Fajar Falah | Ririn Nur Alfiani
Source: International Journal on Advanced Science Engineering and Information Technology (article, 2023-02-04)
Field: Chemistry — Analytical Chemistry

Determination of Total Carotene and Vitamin C in Chili Powder (Capsicum annuum L.) Non-destructively Using Near-Infrared Spectroscopy Chili (Capsicum annuum L.) is an important source of total carotene and vitamin C. Both substances are widely used in food processing materials, supporting a healthy immune system and medicine. However, destructive testing often obtains information about the substances, which damages the tested material and requires a relatively long analysis. Therefore, this research aims to develop calibration models of total carotene and vitamin C in chili powder for non-destructive testing using near-infrared spectroscopy. The samples consist of four groups of color, i.e., light green, dark green, red tinge, and red, with a total of 84 samples. Seventy percent of the sample was used for calibration, while the rest of the sample was used for validation. Spectra were measured using the NIRFlex N-500 instrument at a wavelength of 1000 nm to 2500 nm and analyzed with the partial least square (PLS) method using three spectral pre-treatments, which are multiplicative scatter correction (MSC), first derivative savitzky-golay, and de-trending. The accuracy and model reliability was determined by the coefficient of determination (R2) and the residual predictive deviation (RPD). The best calibration models were successfully obtained when the spectrum was processed using the first derivative savitzky-golay pre-treatment with 6 and 5 PLS factors for vitamin C and total carotene, respectively. Both models were accurate and can be potentially used for determining the total carotene and vitamin C in chili powder samples non-destructively. Carotene ; Calibration ; Partial least squares regression ; Vitamin ; Second derivative ; Analytical Chemistry (journal) ; Mathematics ; Chemistry ; Food science ; Chromatography ; Materials science ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: William Morton | Caoimhe Joyce | Jonny Taylor | Mary P. Ryan | Stefano Angioletti‐Uberti | Fang Xie
Source: The Journal of Physical Chemistry C (article, 2023-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modeling Au Nanostar Geometry in Bulk Solutions The findings within make it possible to reference gold nanostars based on their geometric properties, similar to how a radius describes a nanosphere, rather than just the LSPR of the structure-the current practice. The average tip approximation presented reduces the complexity of nanostars in discrete dipole approximation simulations. By matching the projected area and LSPR of the modeled nanostars to synthesized nanostars, the volume, surface area, and number of tips can be approximated without a lengthy characterization process. Knowing the nanoparticle geometry can determine drug carrier capacity, an approximate number of hot spots for EM imaging, and how the particle will interact with cells. The geometric data obtained will drive the biological application and increase the usability of this particle class. Discrete dipole approximation ; Characterization (materials science) ; Particle (ecology) ; RADIUS ; Nanoparticle ; Usability ; Matching (statistics) ; Dipole ; Surface (topology) ; Nanotechnology ; Geometry ; Materials science ; Process (computing) ; Computer science ; Physics ; Mathematics ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Nanoparticle-Based Drug Delivery
Authors: E. Cardelli | Antonio Faba | Antonino Laudani | S. Quondam Antonio | AbdelRahman M. Ghanim
Source: International Journal of Numerical Modelling Electronic Networks Devices and Fields (article, 2023-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Comparison between different models of magnetic hysteresis in the solution of the TEAM 32 problem Abstract The numerical modeling of magnetic materials in simulators is a difficult task, above all in real devices with specific excitation. The aim of this work is to compare the accuracy of scalar and vector Preisach models in a well know test benchmark: the TEAM 32 problem. The availability of measured data for this benchmark test and the simple geometry allow us to build hysteresis models and to test them in a 2D finite element analysis (FEA) scheme. The specific numerical formulation of each hysteresis model implemented is described, including the technique for the numerical identification of parameters starting from measured data. The comparison among the models is done in terms of accuracy, but also in terms of easy implementation in the FEA scheme and of computational cost. Benchmark (surveying) ; Finite element method ; Scalar (mathematics) ; Computer science ; Hysteresis ; Experimental data ; Scheme (mathematics) ; Simple (philosophy) ; Applied mathematics ; Task (project management) ; Algorithm ; Mathematics ; Mathematical analysis ; Engineering ; Physics ; Structural engineering ; Geometry ; Systems engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Microstructure and Mechanical Properties of Steels
Authors: Aryan Mago | Yeon-Suk Yang | Jae‐Hyuck Shim | Aijaz Ahmad John
Source: Sensors (article, 2023-09-15)
Field: Chemistry — Organic Chemistry

Wearable Device for Cumulative Chlorobenzene Detection and Accessible Mitigation Strategies Chronic exposure to low concentrations of volatile organic compounds (VOCs), such as chlorobenzene, is not being monitored in industrializing countries, although VOC exposure is associated with carcinogenic, organ-toxic, and endocrine-disrupting effects. Current VOC-sensing technologies are inaccessible due to high cost, size, and maintenance or are ineffective due to poor sensitivity or reliability. In particular, marginalized individuals face barriers to traditional prescription VOC treatments due to cost, lack of transportation, and limited access to physicians; thus, alternative treatments are needed. Here, we created a novel cumulative wearable color-changing VOC sensor with a paper-based polydiacetylene sensor array for chlorobenzene. With a single smartphone picture, the sensor displays 14 days of logged chlorobenzene exposure data, interpreted by machine-learning (ML) techniques, including principal component analysis. Further, we explored the efficacy of affordable and accessible treatment options to mitigate a VOC's toxic effects. Vitamin D and sulforaphane are naturally found in cruciferous vegetables, like broccoli, and can be used to treat chlorobenzene-mediated bone degradation. Our platform combines these components into a smartphone app that photographs the sensor's colorimetric data, analyzes the data via ML techniques, and offers accessible treatments based on exposure data. Chlorobenzene ; Wearable computer ; Sulforaphane ; Computer science ; Environmental science ; Embedded system ; Chemistry Polydiacetylene-based materials and applications ; Advanced Chemical Sensor Technologies ; Antimicrobial Peptides and Activities
Authors: Daniel Schmidt | Manasa Medikonda | Michael Rizzolo | Claire Silvestre | Julien Frougier | Andrew S. Greene | Mary Breton | Aron Cepler | Jacob Ofek | Itzik Kaplan | Roy Koret | Igor Turovets
Source: Journal of Micro/Nanopatterning Materials and Metrology (article, 2023-02-21)
Field: Materials Science — Surfaces, Coatings and Films

Spectral interferometry for fully integrated device metrology A spectral interferometry technique called vertical travelling scatterometry (VTS) is introduced, demonstrated, and discussed. VTS utilizes unique information from spectral interferometry and enables solutions for applications that are infeasible with traditional scatterometry approaches. The technique allows for data filtering related to spectral information from buried layers, which can then be ignored in the optical model. Therefore, using VTS, selective analyses of the topmost part of an arbitrarily complex stack are possible within a single metrology step. This methodology helps to overcome geometrical complexities and allows for focusing on parameters of interest through dramatically simplified optical modeling. Such model simplifications are specifically desired for back-end-of-line applications. Three examples are monitored discussed: (i) the critical dimensions (CDs) of a first metal level on top of nanosheet gate-all-around transistor structures, (ii) the thickness of an interlayer dielectric above embedded memory in the active area, and (iii) the CDs of trenches on top of tall stacks in the micrometer range comprising many layered dielectrics. It was found that, in all three cases, data filtering through VTS allowed for a simple optical model capable of delivering parameters of interest. The validity and accuracy of the VTS solution results were confirmed by extensive reference metrology obtained by traditional scatterometry, scanning electron microscopy, and transmission electron microscopy. Furthermore, it was shown that machine learning models trained with VTS filtered data can converge to a robust solution with a smaller dataset compared with models training with traditional scatterometry data. Metrology ; Interferometry ; Computer science ; Optics ; Stack (abstract data type) ; Electronic engineering ; Physics ; Engineering Optical Coatings and Gratings ; Semiconductor materials and devices ; Near-Field Optical Microscopy
Authors: Menghan Chen
Source: Highlights in Science Engineering and Technology (article, 2023-02-28)
Field: Chemistry — Analytical Chemistry

Mobile Phone Price Prediction with Feature Reduction Feature reduction can reduce data dimensionality and streamline model size, which focuses on the high relevance data and inferences the output faster. This paper aims to explore the performance and effectiveness of feature reduction methods that accompany the Multilayer Perceptron classifier in predicting the mobile phone price range. Pearson’s Correlation and Principal Components Analysis are chosen as the feature reduction techniques in the research. The experiment sorts the features in significant order with two distinct methods. The three experimental groups reduce 5 features each time and the control group has no feature selection. Then all the groups use the open dataset to train and test the accuracy and loss through MLP. The result indicates that the feature selected by the correlation coefficient facilitates the accuracy of the classification model. When PCA is implemented and only a few features get reduced, the performance improves a little bit, but when more features are eliminated there are huge negative influences. Pearson’s correlation has a better performance than PCA in this experiment, which achieves 95.8% accuracy and validate the effectiveness of the feature reduction method. Dimensionality reduction ; Computer science ; Feature selection ; Artificial intelligence ; Pattern recognition (psychology) ; Feature (linguistics) ; Pearson product-moment correlation coefficient ; Principal component analysis ; Correlation ; Mobile phone ; Multilayer perceptron ; Data mining ; Feature extraction ; Machine learning ; Artificial neural network ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Face and Expression Recognition
Authors: Du Wang | Xue Li | Fei Ma | Li Yu | Wen Zhang | Jun Jiang | Liangxiao Zhang | Peiwu Li
Source: Applied Sciences (article, 2023-08-21)
Field: Chemistry — Analytical Chemistry

A Rapid and Nondestructive Detection Method for Rapeseed Quality Using NIR Hyperspectral Imaging Spectroscopy and Chemometrics In this study, a fast and non-destructive method was proposed to analyze rapeseed quality parameters with the help of NIR hyperspectral imaging spectroscopy and chemometrics. Hyperspectral images were acquired in the reflectance mode. Meanwhile, the region of interest was extracted from each image by the regional growth algorithm. The kernel partial least square regression was used to build prediction models for crude protein content, oil content, erucic acid content, and glucosinolate content of rapeseed. The results showed that the correlation coefficients were 0.9461, 0.9503, 0.9572, and 0.9335, whereas the root mean square errors of prediction were 0.5514%, 0.5680%, 2.8113%, and 10.3209 µmol/g for crude protein content, oil content, erucic acid content, and glucosinolate content, respectively. It demonstrated that NIR hyperspectral imaging is a promising tool to determine rapeseed quality parameters in a rapid and non-invasive manner. Hyperspectral imaging ; Chemometrics ; Rapeseed ; Partial least squares regression ; Content (measure theory) ; Erucic acid ; Imaging spectroscopy ; Glucosinolate ; Mathematics ; Chemistry ; Artificial intelligence ; Chromatography ; Computer science ; Food science ; Brassica ; Agronomy ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Jia Liu | Suhang He | Hugues Lambert | Tung‐Chun Lee
Source: Frontiers in Chemistry (article, 2023-12-14)
Field: Chemistry — Spectroscopy

Modulation of redox reactivity of resazurin through host-guest complexation with Cucurbit[n]uril (n = 7, 8) Resazurin (Alamar Blue, RZ) is a widely utilized fluorescent probe for biological sensing, whose fluorescent intensity can be modulated by changing its redox states; thereby, electrochemical reactivity of RZ is of significance when designing a sensing assay. Herein, we report novel two-way electrochemical reactivity modulation of RZ using host-guest complexation with rigid molecular containers cucurbit[<i>n</i>]uril (CB<i>n</i>, <i>n</i> = 7, 8). The complexation between CB<i>n</i> and RZ is confirmed by <sup>1</sup>H NMR measurements and supported by computational simulation, and the binding constants are determined via UV-vis titration. Notably, the voltametric data highlights that the redox reactivity of RZ can be activated or deactivated upon encapsulation by CB8 or CB7, respectively. This two-way reactivity modulation is hypothesized to be mediated by the difference in cavity volume that favors or hinders the approach of water molecules to the encapsulated reaction center during the reduction process. Despite the similar cavity size to CB, molecular containers such as cyclodextrins (CDs) exhibit considerably weaker modulation effects. Our approach can potentially be applied to other redox processes that involve proton transfer, and open new possibilities in supramolecular electrochemistry. Resazurin ; Chemistry ; Reactivity (psychology) ; Redox ; Electrochemistry ; Fluorescence ; Supramolecular chemistry ; Titration ; Molecule ; Binding constant ; Photochemistry ; Inorganic chemistry ; Physical chemistry ; Organic chemistry ; Electrode ; Binding site Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: Florence Yerly | Mathias Blaise | Simon Barras
Source: CHIMIA International Journal for Chemistry (article, 2023-09-20)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Melting Point Prediction of Ionic Liquids: CatBoost Approach Using ionic liquids as phase changing materials is of particular interest in the context of heat storage. As a consequence, predicting accurately the melting point of ionic liquids is of capital importance as it is one of the most important thermophysical properties in this context. In this work we consider a data set composed of 2249 different ionic liquids, with a majority of imidazole or ammonium cation-based molecules. We present a free and easy-to-use melting point predictive algorithm built on the CatBoost algorithm, making strong use of molecular descriptors. Based on LASSO, we select the most relevant descriptors for the task at hand and compare the model with previous ones. Melting point ; Context (archaeology) ; Ionic liquid ; Computer science ; Task (project management) ; Set (abstract data type) ; Ionic bonding ; Work (physics) ; Point (geometry) ; Machine learning ; Biological system ; Chemistry ; Artificial intelligence ; Thermodynamics ; Mathematics ; Organic chemistry ; Ion ; Physics ; Engineering Machine Learning in Materials Science ; Ionic liquids properties and applications ; Chemistry and Chemical Engineering
Authors: Temitayo Olugbade | Lili Lin | Alice Sansoni | Nihara Warawita | Yuanze Gan | Xijia Wei | Bruna Petreca | Giuseppe Boccignone | Douglas Atkinson | Youngjun Cho | Sharon Baurley | Nadia Bianchi‐Berthouze
Source:  (article, 2023-09-10)
Field: Materials Science — Polymers and Plastics

FabricTouch: A Multimodal Fabric Assessment Touch Gesture Dataset to Slow Down Fast Fashion Touch exploration of fabric is used to evaluate its properties, and it could further be leveraged to understand a consumer’s sensory experience and preference so as to support them in real time to make careful clothing purchase decisions. In this paper, we open up opportunities to explore the use of technology to provide such support with our FabricTouch dataset, i.e., a multimodal dataset of fabric assessment touch gestures. The dataset consists of bilateral forearm movement and muscle activity data captured while 15 people explored 114 different garments in total to evaluate them according to 5 properties (warmth, thickness, smoothness, softness, and flexibility). The dataset further includes subjective ratings of the garments with respect to each property and ratings of pleasure experienced in exploring the garment through touch. We further report baseline work on automatic detection. Our results suggest that it is possible to recognise the type of fabric property that a consumer is exploring based on their touch behaviour. We obtained mean F1 score of 0.61 for unseen garments, for 5 types of fabric property. The results also highlight the possibility of additionally recognizing the consumer’s subjective rating of the fabric when the property being rated is known, mean F1 score of 0.97 for unseen subjects, for 3 rating levels. Gesture ; Multi-touch ; Computer science ; Gesture recognition ; Artificial intelligence ; Human–computer interaction ; Computer vision ; Computer graphics (images) Textile materials and evaluations ; Hand Gesture Recognition Systems ; Advanced Sensor and Energy Harvesting Materials
Authors: Sihan Zhou | Liang Qin | Hui Sun | Bo Peng | Jiangjun Ruan | Jing Wang | Xu Tang | Xiaole Wang | Kaipei Liu
Source: Sensors (article, 2023-04-19)
Field: Materials Science — Materials Chemistry

TransFNN: A Novel Overtemperature Prediction Method for HVDC Converter Valves Based on an Improved Transformer and the F-NN Algorithm Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer-FCM-NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically. Transformer ; HVDC converter ; High-voltage direct current ; Grid ; Engineering ; Control theory (sociology) ; Voltage ; Computer science ; Electrical engineering ; Mathematics ; Direct current ; Artificial intelligence High voltage insulation and dielectric phenomena ; High-Voltage Power Transmission Systems ; Power Transformer Diagnostics and Insulation