jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David Mareček | Florian Bertram | Petr Mikulı́k | Devanshu Varshney | Jiřı́ Novák | Stefan Kowarik
Source: Journal of Applied Crystallography (article, 2024-03-15)
Field: Materials Science — Surfaces, Coatings and Films

Millisecond X-ray reflectometry and neural network analysis: unveiling fast processes in spin coating X-ray reflectometry (XRR) is a powerful tool for probing the structural characteristics of nanoscale films and layered structures, which is an important field of nanotechnology and is often used in semiconductor and optics manufacturing. This study introduces a novel approach for conducting quantitative high-resolution millisecond monochromatic XRR measurements. This is an order of magnitude faster than in previously published work. Quick XRR (qXRR) enables real time and <i>in situ</i> monitoring of nanoscale processes such as thin film formation during spin coating. A record qXRR acquisition time of 1.4 ms is demonstrated for a static gold thin film on a silicon sample. As a second example of this novel approach, dynamic <i>in situ</i> measurements are performed during PMMA spin coating onto silicon wafers and fast fitting of XRR curves using machine learning is demonstrated. This investigation primarily focuses on the evolution of film structure and surface morphology, resolving for the first time with qXRR the initial film thinning via mass transport and also shedding light on later thinning via solvent evaporation. This innovative millisecond qXRR technique is of significance for <i>in situ</i> studies of thin film deposition. It addresses the challenge of following intrinsically fast processes, such as thin film growth of high deposition rate or spin coating. Beyond thin film growth processes, millisecond XRR has implications for resolving fast structural changes such as photostriction or diffusion processes. X-ray reflectivity ; Reflectometry ; Millisecond ; Thin film ; Materials science ; Spin coating ; Silicon ; Coating ; Evaporation ; Neutron reflectometry ; Nanoscopic scale ; Optics ; Optoelectronics ; Nanotechnology ; Physics ; Computer science ; Scattering ; Time domain Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Advancements in Photolithography Techniques
Authors: Priyanka Dash | Suvranshu Pattanayak | Sayantan Ghosh | Nihar Ranjan Das | Pramod Bhakuni | S. R. Barman | Bismita Nayak
Source: Chemical Engineering Journal (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Bright and photostable MHA derived “luminous pearls” for multi-color bioimaging: An eco-sustainable cradle-to-gate approach guided by GRA coupled ANN Nanotechnology ; Artificial intelligence ; Computer science ; Materials science ; Chemistry Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques
Authors: Supuni. P. Dassanayake | Lakshika S. Nawarathna
Source: Journal of Food Composition and Analysis (article, 2024-11-12)
Field: Chemistry — Inorganic Chemistry

A machine learning-based approach for predicting the level of palm oil adulteration in coconut oil Palm oil ; Coconut oil ; Palm ; Food science ; Coco ; Edible oil ; Mathematics ; Artificial intelligence ; Chemistry ; Computer science ; Physics Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis
Authors: Huan Chen | Liangjie Lin | Lin Chen | Zhong Chen
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network Chemical exchange saturation transfer (CEST) MRI is a versatile technique that exploits the saturation transfer between exchangeable protons and water for non-invasive detection of diluted metabolites. Although theoretically promising, the practical application of CEST MRI is still challenged by low CEST contrast and low signal-to-noise ratio (SNR) of acquired images. Here, we proposed a deep learning-based method, dubbed denoising CEST network (DCEST-Net), to fully exploit the spatiotemporal correlation prior embedded in the CEST images and restore noise-free images from their noisy observations. Results suggested that DCEST-Net can achieve better performance compared to the state-of-the-art denoising methods. Noise reduction ; Computer science ; Artificial intelligence ; Convolution (computer science) ; Pattern recognition (psychology) ; Noise (video) ; Signal-to-noise ratio (imaging) ; Artificial neural network ; Image (mathematics) Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies ; Advanced MRI Techniques and Applications
Authors: Zoé Faure Beaulieu | Volker L. Deringer | Fausto Martelli
Source: The Journal of Chemical Physics (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

High-dimensional order parameters and neural network classifiers applied to amorphous ices Amorphous ice phases are key constituents of water's complex structural landscape. This study investigates the polyamorphic nature of water, focusing on the complexities within low-density amorphous ice (LDA), high-density amorphous ice, and the recently discovered medium-density amorphous ice (MDA). We use rotationally invariant, high-dimensional order parameters to capture a wide spectrum of local symmetries for the characterization of local oxygen environments. We train a neural network to classify these local environments and investigate the distinctiveness of MDA within the structural landscape of amorphous ice. Our results highlight the difficulty in accurately differentiating MDA from LDA due to structural similarities. Beyond water, our methodology can be applied to investigate the structural properties and phases of disordered materials. Amorphous solid ; Optimal distinctiveness theory ; Amorphous ice ; Artificial neural network ; Materials science ; Chemical physics ; Computer science ; Artificial intelligence ; Biological system ; Chemistry ; Crystallography ; Psychology Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Alexandra Day | Carolin B. Wahl | Vishu Gupta | Roberto dos Reis | Wei‐keng Liao | Chad A. Mirkin | Vinayak P. Dravid | Alok Choudhary | Ankit Agrawal
Source: Microscopy and Microanalysis (article, 2024-05-17)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Image Classification for Automated Electron Microscopy Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery. Computer science ; Artificial intelligence ; Machine learning ; False positive paradox ; Intuition ; Big data ; Artificial neural network ; Grayscale ; Field (mathematics) ; Deep learning ; Data mining ; Image (mathematics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Image Processing Techniques and Applications
Authors: Larissa Knierim | Alexander Uhl | A. Schmidt | Marcel Flemming | Theresa Höß | Jonas Treutwein | Jochen Strube
Source: ACS Omega (article, 2024-07-10)
Field: Chemistry — Spectroscopy

Pressurized Hot Water Extraction as Green Technology for Natural Products as Key Technology with Regard to Hydrodistillation and Solid–Liquid Extraction Hydrodistillation and solid-liquid extraction with organic solvents or supercritical CO<sub>2</sub> are standard technologies for natural product manufacturing. Within this technology, portfolio pressurized hot water technology is ranked as a green, sustainable, resilient, kosher and halal manufacturing process. Essential for sustainability is energy integration for heating and cooling the auxiliary water as well as product concentration without evaporation but with the aid of low energy consuming ultra- and nanofiltration membrane technology. The incorporation of modern unit operations, such as pressurized hot water extraction, along with inline measurement devices for Process Analytical Technology approaches, showcases a shift in traditional extraction processes. Traditional equipment and processes still dominate the manufacturing of plant extracts, yet leveraging innovative chemical process engineering methods offers promising avenues for the economic and ecological advancement of botanicals. Techniques such as modeling and process intensification with green technology hold potential in this regard. Digitalization and Industry 4.0 methodologies, including machine learning and artificial intelligence, play pivotal roles in sustaining natural product extraction manufacturing and can profoundly impact the future of human health. Extraction (chemistry) ; Key (lock) ; Natural (archaeology) ; Water extraction ; Chromatography ; Chemistry ; Computer science ; Biology Analytical Chemistry and Chromatography ; Microbial Metabolic Engineering and Bioproduction ; Chromatography in Natural Products
Authors: Rohit ANAND | Roaf Ahmad PARRAY | Indra MANI | Tapan Kumar KHURA | Harilal KUSHWAHA | Brij Bihari SHARMA | Susheel SARKAR | Samarth GODARA | Shideh MOJERLOU | Hasan MIRZAKHANINAFCHI
Source: Frontiers of Agricultural Science and Engineering (article, 2024-06-05)
Field: Chemistry — Analytical Chemistry

A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology This research explored a novel multimodal approach for disease management in cauliflower crops. With the rising challenges in sustainable agriculture, the research focused on a patch spraying method to control disease and reduce crop losses and environmental impact. For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. The chosen model was integrated with a low-volume sprayer (50‒150 L·ha<sup>‒1</sup>), equipped with an electronic control unit for targeted spraying based on sensor-detected regions. The decision tree model achieved 89.9% testing accuracy, while the SVM model achieved 96.7% accuracy using hyperparameters: cost of 10.0 and tolerance of 0.001. The research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input application. Additionally, the optimized sprayer achieved a 72.5% reduction in chemical usage and a significant time-saving of 21.0% compared to a standard sprayer for black rot-infested crops. These findings highlight the potential efficiency and resource conservation benefits of innovative sprayer technology in precision agriculture and disease management. Computer science ; Agricultural engineering ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Soumyadipta Chakraborty | Indrayani Patra | Ardhendu Pal | Koushik Mondal | Manik Pradhan
Source: Infrared Physics & Technology (article, 2024-07-16)
Field: Chemistry — Spectroscopy

Development of a semi-supervised machine learning based noise filter for quantum cascade laser-coupled mid-infrared spectrometer Quantum cascade laser ; Cascade ; Spectrometer ; Infrared ; Noise (video) ; Laser ; Computer science ; Filter (signal processing) ; Quantum ; Optics ; Acoustics ; Physics ; Artificial intelligence ; Computer vision ; Chemistry ; Quantum mechanics Spectroscopy and Laser Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Jeniffer Katerine Carrillo Gómez | Carlos Alberto Cuastumal Vásquez | Cristhian Manuel Durán Acevedo | Jesús Brezmes Llecha
Source: Chemosensors (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

Assessing Data Fusion in Sensory Devices for Enhanced Prostate Cancer Detection Accuracy The combination of an electronic nose and an electronic tongue represents a significant advance in the pursuit of effective detection methods for prostate cancer, a widespread form of cancer affecting men across the globe. These cutting-edge devices, collectively called “E-Senses”, use data fusion to identify distinct chemical compounds in exhaled breath and urine samples, potentially improving existing diagnostic techniques. This study combined the information from two sensory perception devices to detect prostate cancer in biological samples (breath and urine). To achieve this, data from patients diagnosed with the disease and from control individuals were collected using a gas sensor array and chemical electrodes. The signals were subjected to data preprocessing algorithms to prepare them for analysis. Following this, the datasets for each device were individually analyzed and subsequently merged to enhance the classification results. The data fusion was assessed and it successfully improved the accuracy of detecting prostate-related conditions and distinguishing healthy patients, achieving the highest success rate possible (100%) in classification through machine learning methods, outperforming the results obtained from individual electronic devices. Prostate cancer ; Sensor fusion ; Sensory system ; Cancer detection ; Fusion ; Prostate ; Computer science ; Medicine ; Artificial intelligence ; Cancer ; Neuroscience ; Internal medicine ; Psychology Spectroscopy and Chemometric Analyses
Authors: Wei Chen | Jiaqing Shen | Yujie Ke | Yuwei Hu | Lin Wu | Jinfeng Zhu | Zhaogang Dong
Source: World Scientific Annual Review of Functional Materials (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress in Metasurfaces: An Introductory Note from Fundamentals and Design Methods to Applications Metasurfaces refer to the sub-wavelength nanostructures that are capable of manipulating the amplitude, phase, polarization, and other characteristics of light, to enable diverse applications across the ultraviolet, visible, infrared, and terahertz spectra. In this review paper, we aim to provide an introductory note on metasurfaces, from fundamentals and design methods to applications, such as biosensing, environmental monitoring, metalenses, optical cloaking, electromagnetic scattering, structural color, miniaturized devices, and others. Moreover, we also identify the key challenges and limitations of metasurfaces, such as fabrication, integration, optimization, tuning, signal processing, and analysis, and suggest possible directions and solutions for future research. At last, we envision several emerging and promising trends for metasurfaces, such as new materials and structures, new phenomena and mechanisms, machine learning, and artificial intelligence techniques. The review is expected to inspire future development in this exciting and rapidly evolving field of metasurface devices. Terahertz radiation ; Computer science ; Cloaking ; Metamaterial ; Nanotechnology ; Materials science ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Hassan Z. Ghanem | Asmaa A. El‐Zaher | Sally Tarek Mahmoud | Enas A. Taha
Source: BMC Chemistry (article, 2024-10-17)
Field: Chemistry — Analytical Chemistry

Spectrophotometric determination of olanzapine, fluoxetine HCL and its impurity using univariate and chemometrics methods reinforced by latin hypercube sampling: Validation and eco-friendliness assessments Novel univariate and chemometrics-aided UV spectrophotometric methods were tailored to undergo the fundamentals of green and white analytical chemistry for the simultaneous estimation of a ternary mixture of olanzapine (OLA), fluoxetine HCL (FLU), and its toxic impurity 4-(Trifluoromethyl) phenol (FMP) without any prior separation. The dual-wavelength ratio spectrum univariate method was used to determine OLA and FLU in the presence of FMP in the range of (4-20) and (5-50) μg/ml, respectively. In compliance with the International Conference on Harmonization (ICH) standards, the technique was validated and established Remarkable accuracy (98-102%) and precision (< 2%) with limits of quantification (LOQs) of 0.432 and 2.002 μg/ml, respectively. Partial least squares (PLS) and artificial neural networks (ANNs) are chemometric methodologies that have advantages over the univariate method and use significant innovations employing Latin hypercube sampling (LHS), allowing the generation of a reliable validation set to guarantee the effectiveness and sustainability of these models. The concentration ranges used were (2-20), (2-20), and (5-50) μg/ml; for PLS, the LOQs were 0.602, 0.508, and 1.429 μg/ml, and the root mean square errors of prediction (RMSEPs) were 0.087, 0.048, and 0.159 for OLA, FMP, and FLU, respectively; and for ANNs, the LOQs were 0.551, 0.465, and 0.965 μg/ml, with RMSEPs of 0.056, 0.047, and 0.087 for OLA, FMP, and FLU, respectively. The developed methods yield a greener National Environmental Methods Index (NEMI) with an eco-scale assessment (ESA) score of 90 and a complementary Green Analytical Procedure Index (complex GAPI) in quadrants with an analytical greenness metric (AGREE) score of 0.8. The Red‒Green-Blue 12 algorithm (RGB 12) scored 88.9, outperforming on reported methods and demonstrating widespread practical and environmental approval. Statistical analysis revealed no noteworthy differences (P > 0.05) among the proposed and published techniques. Both pure powders and pharmaceutical capsules were analyzed via these methods. Chemometrics ; Olanzapine ; Univariate ; Sampling (signal processing) ; Latin hypercube sampling ; Statistics ; Chromatography ; Chemistry ; Impurity ; Multivariate statistics ; Mathematics ; Analytical Chemistry (journal) ; Computer science ; Psychology ; Organic chemistry ; Psychiatry ; Monte Carlo method ; Schizophrenia (object-oriented programming) Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Electrochemical sensors and biosensors
Authors: Abhishek Ojha | Megha | Satya Bulusu | Arup Banerjee
Source: Computational and Theoretical Chemistry (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

ANN and DFT investigation of 55-atom icosahedral Ag-Pt nanoalloys: Understanding structure, dynamics, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si21.svg" display="inline" id="d1e2404"><mml:msub><mml:mrow><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> activation Adsorption ; Chemistry ; Icosahedral symmetry ; Atom (system on chip) ; Molecular dynamics ; Molecule ; Stability (learning theory) ; Chemical physics ; Nanotechnology ; Crystallography ; Computational chemistry ; Physical chemistry ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; nanoparticles nucleation surface interactions
Authors: Junjie Gong | Jian Xia | Tianci Wang | Zhiyuan Li | Zixuan Wang | Yunxiao Dong | Gongmin Li | Xiangshui Miao | Rui Yang
Source: ACS Photonics (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Six Bit Optical Phase States Realized in Nonvolatile Phase Shifter Based on N-Doped Sb<sub>2</sub>Se<sub>3</sub> Due to the nonvolatile and large optical contrast of phase-change materials (PCMs), PCM-based optical phase shifters have been considered as a powerful technology for a variety of emerging applications, such as optical switching, optical memory, and neuromorphic computing. However, due to the lack of a phase-change material with low-loss and high optical contrast, the phase modulation space of current PCM-based phase shifters still falls short of meeting the requirements for real-world applications. To address these issues, it is necessary to develop a phase-change material with a high optical contrast to enhance the phase modulation capability of optical phase shifters. In this work, we have designed a new optical PCM by doping Sb2Se3 with the nitrogen element (N), which can improve the optical refractive index of Sb2Se3 from 0.67 to 1.10 and keep its optical loss near 0 at communication bands. By integrated N-doped Sb2Se3 with unbalanced Mach–Zehnder interferometer (MZI), an optical phase shifter with a high phase modulation space is successfully developed and achieves 84 distinct optical phase states (>6 bit). Finally, an artificial neural network (ANN) is established using the optical phase shifter as an optical synapse. The multiwavelength and multistate encoding enabled by this optical phase shifter can not only reduce hardware costs by more than 75%, but also maintain a high image recognition rate (96.67%). This work provides an effective approach for designing PCMs with high phase modulation space, which is significant for constructing high-performance programmable optical phase shifts. Phase shift module ; Materials science ; Doping ; Phase (matter) ; Optoelectronics ; Bit (key) ; Optics ; Physics ; Computer science ; Insertion loss Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Zhu Wang | Xianglong Wang | Yuxiang Zheng | Xuemin Du | W. Dai | Yue Wang
Source: Journal of Nuclear Materials (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Prediction of thermal conductivity in UO2 with SiC additions and related decisive features discovery Thermal conductivity ; Materials science ; Nuclear engineering ; Composite material ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications