jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dennis Valbjørn Christensen | U. Staub | T. R. Devidas | Beena Kalisky | Katja C. Nowack | James L. Webb | Ulrik L. Andersen | Alexander Huck | David A. Broadway | Kai Wagner | Patrick Maletinsky | Toeno van der Sar | Chunhui Du | Amir Yacoby | David Collomb | S. J. Bending | Ahmet Oral | Hans J. Hug | Andrada-Oana Mandru | V. Neu | H. W. Schumacher | S. Sievers | Hitoshi Saitô | Alexander A. Khajetoorians | Nadine Hauptmann | Susanne Baumann | Alexander Eichler | Christian L. Degen | Jeffrey McCord | Marc Vogel | M. Fiebig | Peter Fischer | A. Hierro‐Rodríguez | Simone Finizio | S. S. Dhesi | Claire Donnelly | Felix Büttner | Ofer Kfir | Wen Hu | Sergey Zayko | Stefan Eisebitt | Bastian Pfau | Robert Frömter | Mathias Kläui | Fehmi Sami Yasin | Benjamin McMorran | S. Seki | Yu Xia | Axel Lubk | Daniel Wolf | Nini Pryds | Denys Makarov | Martino Poggio
Source: arXiv (Cornell University) (preprint, 2024-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

2024 Roadmap on Magnetic Microscopy Techniques and Their Applications in Materials Science Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetization patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using SQUIDs, spin center and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoMRI. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, 3D and geometrically curved objects of different material classes including 2D materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials. Microscale chemistry ; Nanotechnology ; Spintronics ; Materials science ; Multiferroics ; Magnet ; Engineering physics ; Magnetization ; Magnetic field ; Physics ; Ferromagnetism ; Condensed matter physics ; Engineering ; Optoelectronics ; Mechanical engineering ; Ferroelectricity Multiferroics and related materials ; Characterization and Applications of Magnetic Nanoparticles ; Magnetic and transport properties of perovskites and related materials
Authors: Hongbo Li | Dapeng Jiang | Wanjing Dong | Cheng Jin | Xihai Zhang
Source: Foods (article, 2024-09-09)
Field: Chemistry — Analytical Chemistry

Towards Sustainable and Dynamic Modeling Analysis in Korean Pine Nuts: An Online Learning Approach with NIRS Due to its advantages such as speed and noninvasive nature, near-infrared spectroscopy (NIRS) technology has been widely used in detecting the nutritional content of nut food. This study aims to address the problem of offline quantitative analysis models producing unsatisfactory results for different batches of samples due to complex and unquantifiable factors such as storage conditions and origin differences of Korean pine nuts. Based on the offline model, an online learning model was proposed using recursive partial least squares (RPLS) regression with online multiplicative scatter correction (OMSC) preprocessing. This approach enables online updates of the original detection model using a small amount of sample data, thereby improving its generalization ability. The OMSC algorithm reduces the prediction error caused by the inability to perform effective scatter correction on the updated dataset. The uninformative variable elimination (UVE) algorithm appropriately increases the number of selected feature bands during the model updating process to expand the range of potentially relevant features. The final model is iteratively obtained by combining new sample feature data with RPLS. The results show that, after OMSC preprocessing, with the number of features increased to 100, the new online model's R2 value for the prediction set is 0.8945. The root mean square error of prediction (RMSEP) is 3.5964, significantly outperforming the offline model, which yields values of 0.4525 and 24.6543, respectively. This indicates that the online model has dynamic and sustainable characteristics that closely approximate practical detection, and it provides technical references and methodologies for the design and development of detection systems. It also offers an environmentally friendly tool for rapid on-site analysis for nut food regulatory agencies and production enterprises. Computer science ; Partial least squares regression ; Preprocessor ; Random forest ; Artificial intelligence ; Feature (linguistics) ; Generalization ; Data pre-processing ; Sample (material) ; Data mining ; Process (computing) ; Machine learning ; Set (abstract data type) ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Nitin Wange | Sachin Chaudhari | Koteswararao Seelam | S. Koteswari | T. Ravichandran | B Manivannan
Source: THE SCIENTIFIC TEMPER (article, 2024-03-15)
Field: Materials Science — Polymers and Plastics

Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling This research presents a novel algorithmic material selection framework for wearable medical devices, utilizing a genetic algorithm-based approach with multiscale modeling. The study employs a comprehensive research methodology encompassing computational modeling, data visualization, and performance assessment. Initially, a diverse set of materials is defined, and their performance scores are assigned to establish a baseline for evaluation. The ensuing data visualization includes a bar chart, a scatter plot, and a line chart, providing insights into material performance, cost-performance relationships, and the convergence of the genetic algorithm, respectively. Performance metrics such as accuracy, precision, and recall are calculated to gauge the algorithm’s efficacy, presented in a bar chart for a nuanced evaluation. Furthermore, a receiver operating characteristic (ROC) curve and a confusion matrix are employed for discriminative ability assessment and detailed classification performance analysis. The results showcase the algorithm’s proficiency in material selection, emphasizing the importance of accuracy, precision, and recall in the complex landscape of wearable medical device development. The abstract concludes with a summary of the implications drawn from each visualization, highlighting the potential of the proposed algorithmic framework to enhance the precision and efficiency of material selection processes for wearable medical devices. This research contributes to the advancement of materials science in healthcare applications, presenting a holistic approach that integrates computational techniques and data-driven methodologies for optimized material selection Computer science ; Selection (genetic algorithm) ; Wearable computer ; Genetic algorithm ; Wearable technology ; Algorithm ; Artificial intelligence ; Machine learning ; Embedded system Textile materials and evaluations
Authors: MOHD IZANI OTHMAN | Nurul Izza Husin | Ainnie Rahayu Abdullah | Nor Aziyah Bakhari | Azrinawati Mohd Zin | Marina Mokhtar
Source: Quantum Journal of Social Sciences and Humanities (article, 2024-04-28)
Field: Chemistry — Physical and Theoretical Chemistry

A JOURNEY INTO THE THRILLING DEPTHS OF CHEMISTRY THROUGH GAMIFICATION In the realm of Pre-Diploma in Science education, cultivating an engaging and interactive learning environment stands as a pivotal factor for student success. Acknowledging the centrality of science as a core subject, this study investigates the potential of gamification to enrich the learning experience and enhance scientific thinking skills among students. The primary objectives involve assessing the impact of CHEM-XPLORACE, a gamified science activity, on students' motivation and learning in the Chemistry subject. Specifically, the study aims to explore students' responses to the integration of gamification in science education. Data were collected from 70 Pre-Diploma in Science students at a branch of Universiti Teknologi MARA, employing a combination of observation and questionnaire survey. Through their participation in CHEM-XPLORACE activities, students' experiences, perceptions, and learning outcomes were systematically gathered and analyzed. The findings uncovered a positive correlation between the implementation of CHEM-XPLORACE and heightened student enjoyment and motivation in learning science, particularly Chemistry. Moreover, students reported an enhancement in their ability to retain scientific facts and knowledge, attributed to their engagement in the gamified activities. The observed increase in motivation and learning outcomes underscores the effectiveness of gamification in Chemistry teaching and learning. The incorporation of interactive and enjoyable elements in learning environments can contribute to a more positive attitude towards science, fostering a deeper understanding of the subject matter. The study recommends educators consider integrating gamification strategies to enhance science education in Pre-Diploma programs. Chemistry ; Computer science ; Psychology Various Chemistry Research Topics
Authors: Patrik Sioris | Meri Mäkelä | Anton Kontunen | Markus Karjalainen | Antti Vehkaoja | Niku Oksala | Antti Roine
Source: International Journal of Molecular Sciences (article, 2024-10-13)
Field: Chemistry — Spectroscopy

Identification of Phospholipids Relevant to Cancer Tissue Using Differential Ion Mobility Spectrometry Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or vibrational energy to heat tissues, which causes intra- and extracellular water to expand rapidly and degrade cell structures, bursting the cells, which causes the formation of a tissue aerosol or smoke depending on the amount of energy used. This gas phase analyte can then be analyzed via gas analysis methods. Differential mobility spectrometry (DMS) is a method that can be used to differentiate malignant tissue from benign tissues in real time via the analysis of surgical smoke produced by energy instruments. Previously, the DMS identification of cancer tissue was based on a 'black box method' by differentiating the 2D dispersion plots of samples. This study sets out to find datapoints from the DMS dispersion plots that represent relevant target molecules. We studied the ability of DMS to differentiate three subclasses of phospholipids (phosphatidylcholine, phosphatidylinositol, and phosphatidylethanolamine) from a control sample using a bovine skeletal muscle matrix with a 5 mg addition of each phospholipid subclass to the sample matrix. We trained binary classifiers using linear discriminant analysis (LDA) and support vector machines (SVM) for sample classification. We were able to identify phosphatidylcholine, -inositol, and -ethanolamine with SVM binary classification accuracies of 91%, 73%, and 66% and with LDA binary classification accuracies of 82%, 74%, and 72%, respectively. Phosphatidylcholine was detected with a reliable classification accuracy, but ion separation setups should be adjusted in future studies to reliably detect other relevant phospholipids such as phosphatidylinositol and phosphatidylethanolamine and improve DMS as a microanalysis method and identify other phospholipids relevant to cancer tissue. Phosphatidylethanolamine ; Phospholipid ; Phosphatidylcholine ; Chemistry ; Analyte ; Linear discriminant analysis ; Chromatography ; Biochemistry ; Artificial intelligence ; Computer science ; Membrane Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Tongxun Zhao | Pengji Ding | Yishan Wang | Renjun Yang | Sijie Yan | Mingyang Zhuzou | Shaohua Sun | Zuoye Liu | Bitao Hu
Source: Journal of Physics D Applied Physics (article, 2024-01-12)
Field: Chemistry — Spectroscopy

Deep learning assisted pressure measurements using femtosecond laser-induced grating scattering technique Abstract The femtosecond laser-induced grating scattering (fs-LIGS) technique has recently been developed and applied for temperature and pressure measurements. In this work, we combined deep learning with the fs-LIGS technique to predict the gas-phase pressure from raw signals without data post-processing. Two deep learning models, a fully connected neural network and a convolutional neural network, were trained to master the hidden relationship between the features of the raw signal traces and the corresponding pressure under which the signal was recorded. Accurate pressure predictions by both models were achieved as mean percentage errors in model-predicted pressures compared to actual values within −4%–2%. These results suggest the feasibility of combining the deep-learning concept with the fs-LIGS technique for instantaneous pressure determination. Given the proper training of the models, this strategy could be extended to the simultaneous measurement of multiple thermodynamic quantities in real-time combustion and reacting flow diagnostics. Femtosecond ; Grating ; Convolutional neural network ; SIGNAL (programming language) ; Artificial neural network ; Scattering ; Laser ; Deep learning ; Artificial intelligence ; Computer science ; Materials science ; Optics ; Physics Spectroscopy and Laser Applications ; Combustion and flame dynamics ; Advanced Sensor Technologies Research
Authors: Kazutaka Sonobe | Satoshi Tominaka | Akihiko Machida
Source: Research Square (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

X-ray Electronic Pair Distribution Functions for Validating Experimental and Theoretical Electronic Densities of Materials <title>Abstract</title> The states and properties of materials are governed by the constituent atomic structures defined by the positions of nuclei and electrons. Although quantum chemical simulations and other measurements, such as synchrotron X-ray scattering, are used to elucidate the atomic structures of materials under development, experimental data are required for verification and adjustment. Scattering measurements, based on empirical models focused on nucleus positions, are at the core of experimental analyses. However, structure determination way sensitive to the electron shape is limited and not applicable to materials in any state of matter. In this study, electronic pair distribution functions were derived from X-ray scattering data and were used to validate the electron positions in covalent materials on a sub-angstrom scale. This approach is expanded from atomic pair distribution functions analysing wide materials including crystalline and non-crystalline materials, and enables direct comparison of experimental and theoretical electronic structures to validate material's structures beyond atomic configurations. Distribution (mathematics) ; Statistical physics ; Computational physics ; X-ray ; Physics ; Materials science ; Mathematics ; Nuclear physics ; Mathematical analysis X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: María Isabel Sánchez‐Rodríguez | Elena Sánchez‐López | Alberto Marinas | José María Caridad y Ocerín | Francisco J. Urbano
Source: ACS Agricultural Science & Technology (article, 2024-10-11)
Field: Chemistry — Analytical Chemistry

Agro-Climatic Information to Enhance the Machine-Learning Classification of Olive Oils from Near-Infrared Spectra The integrity of extra virgin olive oil (EVOO) quality markers can be compromised owing to deceptive marketing practices, such as misleading geographical origin claims or counterfeit certification labels, i.e., protected designations of origin (PDO). Therefore, it is imperative to introduce ecofriendly, rapid, and economical analytical methods for authenticating EVOO, such as near-infrared (NIR) spectroscopy. Unlike traditional techniques such as chromatography, NIR spectra contain unresolved bands; hence, chemometric tools such as principal component analysis (PCA) are essential for extracting valuable information from them. Herein, PCA was employed to reduce the high dimensionality of the NIR spectra. The PCA factors were then integrated as explanatory variables in machine-learning classification models, enabling the classification of EVOO based on its geographical origin or PDO. Furthermore, the classification models were improved by incorporating agro-climatic data, resulting in a noticeable improvement in the accuracy and reliability of the results. These results were cross-validated by changing the calibration and validation subsamples in successive iterations and averaging the obtained ratios. The results were robust when the olive varieties differed. Consequently, our findings highlight the potential benefits of incorporating agro-climatic information with NIR spectral data in classification models. Olive oil ; Infrared ; Infrared spectroscopy ; Artificial intelligence ; Environmental science ; Machine learning ; Computer science ; Chemistry ; Food science ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Rui Liu | Yuanpeng Li | Tinghui Li | Ping Liu | Wenchang Huang | Lingli Liu | Rui Zeng | Yisheng Hua | Jian Tang | Junhui Hu
Source: Journal of Food Science (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

A rapid, non‐destructive, and accurate method for identifying citrus granulation using Raman spectroscopy and machine learning Citrus fruits are widely consumed for their nutritional value and taste; however, juice sac granulation during fruit storage poses a significant challenge to the citrus industry. This study used Raman spectroscopy coupled with machine learning algorithms to rapidly, non-destructively, and precisely detect citrus granulation. The investigation analyzed 969 Raman spectral data points, comprising 714 non-granulated and 255 granulated citrus samples. Following logistic regression, decision tree, and partial least squares discriminant analyses, the optimal model was refined using principal component analysis, a successive projection algorithm, and a competitive adaptive reweighted sampling algorithm (CARS). The identified characteristic Raman peaks at certain wavenumbers were used as input data for the classification model, revealing differences in the water, ferulic acid, and sugar contents between granulated and non-granulated samples. The partial least squares discriminant classification model achieved an accuracy rate of 0.997, recall rate of 0.994, and F-fraction of 0.996 after preprocessing the standard deviation data and selecting 22 optimal principal components. The critical peaks extracted from the citrus Raman spectra were those at wavenumbers of 1580 and 1661 cm<sup>-1</sup>. The classification model based on combined second derivative-CARS-partial least squares discriminant analysis exhibited the best performance, achieving 100% accuracy for all test sets. The proposed method provides a scientifically robust and reliable means of assessing the quality of an entire citrus crop. Reduced wastage and economic losses, and the related environmental effects of food waste. PRACTICAL APPLICATION: The proposed methods can determine if citrus fruit has become granulated during storage. Additionally, they provide technical support for screening granulated citrus in a pipeline, thereby providing a more scientific and reliable classification of the quality of a citrus crop. Raman spectroscopy ; Granulation ; Computer science ; Artificial intelligence ; Materials science ; Biological system ; Pattern recognition (psychology) ; Process engineering ; Optics ; Engineering ; Composite material ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Dharmendr Kumar | Venkata Muralidhar K | Vinay Jain | Beena Rai
Source: npj Materials Degradation (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Accelerating corrosion inhibitor discovery through computational routes: a case of naphthalene 1-thiocarboxamide Abstract The conventional approach to the discovery of corrosion inhibitors is time-consuming and requires a significant amount of resources. In the present study, we highlight the use of a first principles DFT-based approach to expedite the rational design and discovery of corrosion inhibitors for mild steel in acidic media. From among various sulfur containing molecules shortlisted based on quantum chemical descriptors, naphthalene 1-thiocarboxamide (NTC) is found to have the lowest E LUMO and E gap , suggesting best corrosion inhibition. Subsequently, explicit adsorption studies reveal strong chemisorption of NTC onto the Fe (001) surface, characterized by a plethora of Fe-C/N/S covalent bonds. DFT Surface coverage studies additionally indicate the formation of a compact monolayer of NTC on the Fe surface. Gravimetric, potentiodynamic polarization, and Electrochemical Impedance Spectroscopy studies, all confirm NTC as a remarkable inhibitor for mild steel in 1 N HCl at both room and elevated (60 °C) temperatures even at merely 1 mM concentration. Chemisorption ; Corrosion ; Gravimetric analysis ; Dielectric spectroscopy ; Naphthalene ; Adsorption ; Monolayer ; Corrosion inhibitor ; Molecule ; HOMO/LUMO ; Covalent bond ; Electrochemistry ; Chemistry ; Materials science ; Computational chemistry ; Chemical engineering ; Physical chemistry ; Nanotechnology ; Organic chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Szymon K. Kordylewski | Ryszard Bugno | Andrzej J. Bojarski | Sabina Podlewska
Source: Pharmacological Reports (article, 2024-07-06)
Field: Chemistry — Organic Chemistry

Uncovering the unique characteristics of different groups of 5-HT5AR ligands with reference to their interaction with the target protein The findings from the molecular modeling study provide valuable insights that can guide medicinal chemists in the development of new 5-HT<sub>5A</sub>R ligands. Considering the pharmacological significance of these compounds, they have the potential to become impactful treatments for individuals and communities in the future. Understanding how different crystal/cryo-EM structures of 5-HT<sub>5A</sub>R affect molecular modeling experiments could have major implications for future computational studies on this receptor. Pharmacophore ; Computational biology ; Molecular dynamics ; Identification (biology) ; Docking (animal) ; Chemistry ; Ligand (biochemistry) ; Pharmacology ; Receptor ; Psychology ; Stereochemistry ; Biology ; Biochemistry ; Computational chemistry ; Medicine Chemical synthesis and alkaloids ; Neurotransmitter Receptor Influence on Behavior ; Receptor Mechanisms and Signaling