jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prasenjit Sarkar | Litty Thomas Manamel | Puranjay Saha | Chinmay Jana | Amrit Sarmah | Kannan Udaya Mohanan | Bikas C. Das | Chandan Mukherjee
Source: Materials Horizons (article, 2024-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A triradical-containing trinuclear Pd(<scp>ii</scp>) complex: spin-polarized electronic transmission, analog resistive switching and neuromorphic advancements Neuromorphic computation has emerged as a potential alternative to subvert the von Neumann bottleneck issue in conventional computing. In this context, the development of resistive switching-based memristor devices mimicking various synaptic functionalities has engendered paramount attention. Here, we report a triradical-containing trinuclear Pd(II) cluster with a cyclohexane-like framework constituted by the Pd-Se coordination motif displaying facile memristor property with neuromorphic functionality as a thin-film device. The metal-ligand complex (complex 1) possessed an <i>S</i><sub>t</sub> = 1/2 ground state by experiencing a spin-frustrated-type magnetic coupling phenomenon amongst the three ligand-based organic radicals (<i>S</i><sub>R</sub> = 1/2), coordinated to the Pd(II) ions. Three reversible one-electron reduction waves countered with a one-electron and one two-electron reversible oxidation waves were noticed in the cyclic voltammogram of the complex, confirming electrons accepting and releasing capacity of the complex at low potentials, <i>i.e.</i>, within +0.2 V to -1.1 V. Employing the radical-containing complex 1 as the active thin-film sandwiched between two orthogonal electrodes, resistive switching based memristor property with biological synaptic actions were successfully emulated. Intriguingly, the artificial neural network (ANN) simulated efficient pattern recognition demonstrated using the recorded potentiation and depression curves from the device, which is a step ahead for the hardware realization of neuromorphic computing. The performance of the ANN on MNIST data with reduced image resolution has further been evaluated. Density functional theory (DFT)-based theoretical calculation predicted that the spin-polarized electronic transmission substantiated the memristive property in the neutral complex 1. Neuromorphic engineering ; Transmission (telecommunications) ; Materials science ; Resistive touchscreen ; Spin (aerodynamics) ; Optoelectronics ; Nanotechnology ; Physics ; Electrical engineering ; Computer science ; Artificial neural network ; Engineering Magnetism in coordination complexes ; Electron Spin Resonance Studies ; Lanthanide and Transition Metal Complexes
Authors: Nausheen Yasin | Syed Mumtaz Danish Naqvi | Syed Mamnoon Akhter
Source: Heliyon (article, 2024-02-01)
Field: Chemistry — Analytical Chemistry

Simultaneous spectrophotometric determination of Co (II) and Co (III) in acidic medium with partial least squares regression and artificial neural networks This study aims at the application of two chemometric techniques to visible spectra of acetic acid solutions of Co (II) and Co (III) for simultaneous determination thereof. Spectral data of 145 samples in the range of 400-700 nm were used to build the models. Partial least squares regression models were developed for which latent variables were determined using internal cross-validation with a leave-one-out strategy and 3 and 2 latent variables were selected for Co(II) and Co(III) based on root mean square error of cross-validation. For these models, root mean square errors of prediction were 1.16 and 0.536 mM and coefficients of determination were 0.975 and 0.892 for Co (II) and Co (III). As an alternate method, artificial neural networks consisting of three layers, with 10 neurons in hidden layer, were trained to model spectra and concentrations of cobalt species. Levenberg-Marquardt algorithm with feed-forward back-propagation learning resulted root mean square errors of prediction of 0.316 and 0.346 mM for Co (II) and Co (III) respectively and coefficients of determination were 0.996 and 0.988. Partial least squares regression ; Mean squared error ; Artificial neural network ; Latent variable ; Root mean square ; Mathematics ; Chemistry ; Analytical Chemistry (journal) ; Regression ; Cobalt ; Mean absolute error ; Linear regression ; Regression analysis ; Least-squares function approximation ; Statistics ; Backpropagation ; Biological system ; Chromatography ; Artificial intelligence ; Computer science ; Physics ; Inorganic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Fault Detection and Control Systems
Authors: Min Hou | Xiaoling Jing | Guoen Wen | Haibing He | Shuanhu Gao
Source: Nature Communications (article, 2024-11-07)
Field: Chemistry — Organic Chemistry

Catalyst-free and wavelength-tuned glycosylation based on excited-state intramolecular proton transfer The chemoselectivity of organic reactions is a fundamental topic in organic chemistry. In the long history of chemical synthesis, achieving chemoselectivity is mainly limited to thermodynamic conditions by an exogenous activation strategy. Here, we design an endogenous activation method, which can be used to control the chemoselectivity of phenol and naphthol through the photo-induced excited-state intramolecular proton transfer (ESIPT). A wavelength-tuned glycosylation is developed to showcase the penitential of this new strategy. Traditionally, an exogenous activator (electrophilic promoters) is essential to induce the cleave of a polar single bond, and this strategy has been extensively studied and used in the glycosylation chemistry, for the formation of oxocarbenium cation intermediate. In our systems, the oxocarbenium cation intermediates can be selectively formed from glycosyl donors bearing tunable chromophoric groups under mild conditions of acid-base free and redox neutrality, which enables continuous synthesis of oligosaccharides. Intramolecular force ; Catalysis ; Excited state ; Glycosylation ; Proton ; Photochemistry ; Wavelength ; Chemistry ; Materials science ; Chemical physics ; Physics ; Optoelectronics ; Atomic physics ; Stereochemistry ; Biochemistry Radical Photochemical Reactions ; Photochemistry and Electron Transfer Studies ; Porphyrin and Phthalocyanine Chemistry
Authors: Robert Geitner | Timo Schuett | Stefan Zechel | Ulrich S. Schubert
Source: Chemistry - A European Journal (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Advancements and Challenges in the Synthesis of Oxymethylene Ethers (OMEs) as Sustainable Transportation Fuels The urgent need for sustainable alternatives to fossil fuels in the transportation sector is driving research into novel energy carriers that can meet the high energy density requirements of heavy-duty vehicles without exacerbating the climate change. This concept article examines the synthesis, mechanisms, and challenges associated with oxymethylene ethers (OMEs), a promising class of synthetic fuels potentially derived from carbon dioxide and hydrogen. We highlight the importance of OMEs in the transition towards non-fossil energy sources due to their compatibility with the existing Diesel infrastructure and their cleaner combustion profile. The synthesis mechanisms, including the Schulz-Flory distribution and its implications for OME chain length specificity, and the role of various catalysts and starting materials are discussed in depth. Despite advancements in the field, significant challenges remain, such as overcoming the Schulz-Flory distribution, efficiently managing water as an undesirable byproduct, and improving the overall energy efficiency of the OME synthesis. Addressing these challenges is crucial for OMEs to become a viable alternative fuel, contributing to the reduction of greenhouse gas emissions and the transition to a sustainable energy future in the transportation sector. Fossil fuel ; Greenhouse gas ; Environmental science ; Energy carrier ; Environmental economics ; Biochemical engineering ; Natural resource economics ; Business ; Waste management ; Renewable energy ; Engineering ; Economics ; Ecology Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Carbon dioxide utilization in catalysis
Authors: Daniele Lanzoni | Fabrizio Rovaris | Luis Martín‐Encinar | Andrea Fantasia | Roberto Bergamaschini | Francesco Montalenti
Source: APL Machine Learning (article, 2024-08-29)
Field: Materials Science — Materials Chemistry

Accelerating simulations of strained-film growth by deep learning: Finite element method accuracy over long time scales A convolutional neural network is trained on a large dataset of suitably randomized film profiles and corresponding elastic energy densities ρɛ, computed by the finite element method. The trained model provides quantitative predictions of ρɛ for arbitrary profiles, surrogating its explicit calculation, and is used for the time integration of partial differential equations describing the evolution of strained films. The close match found between the neural network predictions and the “ground-truth” evolutions obtained by the finite element method calculation of ρɛ, even after tens-of-thousands of integration time-steps, validates the approach. A substantial computational speed up without significant loss of accuracy is demonstrated, allowing for million-steps simulations of islands growth and coarsening. The intriguing possibility of extending the domain size is also discussed. Finite element method ; Convolutional neural network ; Computer science ; Time domain ; Artificial neural network ; Partial differential equation ; Ground truth ; Domain (mathematical analysis) ; Algorithm ; Applied mathematics ; Mathematical analysis ; Mathematics ; Artificial intelligence ; Structural engineering ; Engineering Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Theoretical and Computational Physics
Authors: Subhrajit Konwar | Sirin Siyahjani Gültekin | Burak Gültekin | Sushant Kumar | Vinay Deep Punetha | M. Z. A. Yahya | Markus Diantoro | Famiza Abdul Latif | I. M. Noor | Pramod K. Singh
Source: ACS Omega (article, 2024-09-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Stable Efficient Solid-State Supercapacitors and Dye-Sensitized Solar Cells Using Ionic Liquid-Doped Solid Biopolymer Electrolyte As synthetic and nonbiodegradable compounds are becoming a great challenge for the environment, developing polymer electrolytes using naturally occurring biodegradable polymers has drawn considerable research interest to replace traditional aqueous electrolytes and synthetic polymer-based polymer electrolytes. This study shows the development of a highly conducting ionic liquid (1-hexyl-3-methylimidazolium iodide)-doped corn starch-based polymer electrolyte. A simple solution cast method is used to prepare biopolymer-based polymer electrolytes and characterized using different electrical, structural, and photoelectrochemical studies. Prepared polymer electrolytes are optimized based on ionic conductivity, which shows an ionic conductivity as high as 1.90 × 10<sup>-3</sup> S/cm. Fourier transform infrared spectroscopy (FTIR) confirms the complexation and composite nature, while X-ray diffraction (XRD) and polarized optical microscopy (POM) affirm the reduction of crystallinity in biopolymer electrolytes after doping with ionic liquid (IL). Thermal and photoelectrochemical studies further affirm that synthesized material is well stable above 200 °C and shows a wide electrochemical window of 3.91 V. The ionic transference number measurement (<i>t</i> <sub>ion</sub>) confirms the predominance of ionic charge carriers in the present system. An electric double-layer capacitor (EDLC) and a dye-sensitized solar cell (DSSC) were fabricated by using the highest conducting corn starch polymer electrolyte. The fabricated EDLC and DSSC delivered an average specific capacitance of 130 F/g and an efficiency of 1.73% in one sun condition, respectively. Biopolymer ; Supercapacitor ; Dye-sensitized solar cell ; Electrolyte ; Ionic liquid ; Quasi-solid ; Solid-state ; Doping ; Materials science ; Chemical engineering ; Chemistry ; Capacitance ; Optoelectronics ; Polymer ; Electrode ; Composite material ; Organic chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Battery Materials and Technologies
Authors: Yue Wang | Yuanzhong Wang
Source: Journal of Food Science (article, 2024-10-01)
Field: Chemistry — Analytical Chemistry

Feasibility study on discrimination of <i>Polygonatum kingianum</i> origins by NIR and MIR spectra data Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850 cm<sup>-1</sup> band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum. Residual ; Principal component analysis ; Convolutional neural network ; Sensor fusion ; Near-infrared spectroscopy ; Sensitivity (control systems) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Spectroscopy ; Artificial neural network ; Fusion ; Spectral line ; Variable (mathematics) ; Relation (database) ; Statistics ; Mathematics ; Data mining ; Algorithm ; Physics ; Optics ; Engineering Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ekaterina A. Pecherskaya | Pavel E. Golubkov | С. В. Коновалов | S. A. Gurin | M. D. Novichkov
Source: REVIEWS ON ADVANCED MATERIALS SCIENCE (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

Mechanism, models, and influence of heterogeneous factors of the microarc oxidation process: A comprehensive review Abstract The shortcomings of the microarc oxidation (MAO) technology are revealed, in accordance with which a range of topical scientific problems that need to be solved is formulated. It is shown that, despite significant progress in this area of research, there are still controversial issues related mainly to the features of the emergence and combustion of microdischarges. In order to develop a scientifically substantiated approach to the choice of the optimal mode for deposition of microarc oxide coatings, regularities in the influence of heterogeneous factors on their properties are systematized. The analysis of scientific works devoted to the mathematical description of the MAO process has been carried out. It is shown that the existing mathematical models have limitations, and are applicable only to certain aspects of coating formation and do not describe the MAO process as a whole. The prerequisites, recommendations, and theoretical provisions that can be used in the development of a complex mathematical model of the MAO process are revealed. Known methods of analytical description of the forming curve are considered. The possibility of using the forming curve for determining the correct choice of technological parameters and predicting the oxide coating properties during their formation is shown. Mechanism (biology) ; Process (computing) ; Materials science ; Process management ; Chemical engineering ; Computer science ; Process engineering ; Business ; Engineering Corrosion Behavior and Inhibition ; Anodic Oxide Films and Nanostructures ; High-Temperature Coating Behaviors
Authors: Vahid Salehi | Mohaddeseh Izadkhah | Hanifeh Salehi | Niki Sadeghi Pour | Parizad Ghanbarikondori
Source: Asian Pacific Journal of Cancer Biology (article, 2024-05-26)
Field: Materials Science — Biomaterials

The Application of Polybutyl Cyanoacrylate (PBCA) Nanoparticles in Delivering Cancer Drugs Overview: Cancer remains a significant global health challenge, accounting for one in eight deaths worldwide. Chemotherapy, the primary cancer treatment, is often limited by drug resistance. Optimizing therapy procedures has been suggested to improve survival and quality of life for cancer patients, including those with drug resistance. In recent years, nanotechnology has garnered significant attention for its potential in cancer treatment. Nanoparticles (NPs) are widely utilized to enhance therapeutic outcomes by improving drug bioavailability, solubility, and retention time. NPs offer essential conditions for targeted drug delivery through various delivery systems, including microcapsules or NPs. Polybutyl cyanoacrylate (PBCA) is among the most commonly used carriers for drug delivery in cancer treatment, with several studies investigating its efficacy. Methods: This article aims to review the effectiveness of PBCA as a drug delivery system in the treatment of various cancers. Results: Several studies have utilized PBCA for drug delivery in cancer treatment, indicating its potential efficacy in this regard. Conclusion: The review highlights the potential of PBCA as a promising drug delivery system for the treatment of different types of cancer. Cyanoacrylate ; Nanoparticle ; Cancer ; Nanotechnology ; Chemistry ; Medicine ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment
Authors: Sami M. Ibn Shamsah
Source: Inorganics (article, 2024-03-09)
Field: Materials Science — Materials Chemistry

Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods Eco-friendly magnesium-based thermoelectric materials have recently attracted significant attention in green refrigeration technology and wasted heat recovery applications due to their cost effectiveness, non-toxicity, and earth abundance. The energy conversion efficiency of these thermoelectric materials is controlled by a dimensionless thermoelectric figure of merit (TFM), which depends on thermal and electrical conductivity. The independent tuning of the electrical and thermal properties of these materials for TFM enhancement is challenging. The improvement in the TFM of magnesium thermoelectric materials through scattering and structural engineering is experimentally challenging, especially if multiple elements are to be incorporated at different concentrations and at different doping sites. This work models the TFM of magnesium-based thermoelectric materials with the aid of single-hidden-layer extreme learning machine (ELM) and hybrid genetic-algorithm-based support vector regression (GSVR) algorithms using operating absolute temperature, elemental ionic radii, and elemental concentration as descriptors. The developed TFM-G-GSVR model (with a Gaussian mapping function) outperforms the TFM-S-ELM model (with a sine activation function) using magnesium-based thermoelectric testing samples with improvements of 17.06%, 72%, and 73.03% based on correlation coefficient (CC), root mean square error (RMSE), and mean absolute error (MAE) assessment metrics, respectively. The developed TFM-P-GSVR (with a polynomial mapping function) also outperforms TFM-S-ELM during the testing stage, with improvements of 14.59%, 55.31%, and 62.86% using CC, RMSE, and MAE assessment metrics, respectively. Also, the developed TFM-G-ELM model (with a sigmoid activation function) shows superiority over the TFM-S-ELM model with improvements of 14.69%, 79.52%, and 83.82% for CC, RMSE, and MAE assessment yardsticks, respectively. The dependence of some selected magnesium-based thermoelectric materials on temperature and dopant concentration on TFM was investigated using the developed model, and the predicted patterns align excellently with the reported values. This unique performance demonstrated that the developed intelligent models can strengthen room-temperature magnesium-based thermoelectric materials for industrial and technological applications in addressing the global energy crisis. Magnesium ; Thermoelectric effect ; Materials science ; Efficient energy use ; Energy conversion efficiency ; Energy transformation ; Energy (signal processing) ; Process engineering ; Computer science ; Engineering physics ; Optoelectronics ; Thermodynamics ; Electrical engineering ; Metallurgy ; Physics ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; MXene and MAX Phase Materials
Authors: Mahdi Sharifi | Rouein Halladj | Sima Askari
Source: Scientific Reports (article, 2024-01-19)
Field: Chemistry — Inorganic Chemistry

Production of eco friendly DME fuel over sonochemically synthesized UiO66 catalyst The ultrasound-assisted preparation of UiO-66 was carried out at T = 80-220 °C, and the catalytic performances were evaluated in methanol conversion. Also, physicochemical properties were assessed by XRD, SEM, PSD, FTIR, N<sub>2</sub> adsorption-desorption, TG-DTG, and NH<sub>3</sub>-TPD analysis. The characterization proved that increasing the synthesis temperature positively affected the crystallinity, specific surface area, thermal stability, and acidity of the catalysts. Besides, the catalysts' performance was investigated in the methanol conversion reaction (T = 350-450 °C, P = 1 atm, and WHSV = 5 h<sup>-1</sup>), leading to the DME (Dimethyl Ether) production. Rising reaction temperature increased the methanol conversion and DME yield. The synthesized sample at 220 °C had the best properties and performance with conversion and yield of about 38% and 51%, respectively. The stability test for the UiO-66-220 (University of Oslo 66) catalyst was performed at 450 °C for 12 h, and the activity remained stable for about 5 h. Furthermore, the used catalyst was characterized via XRD and TG analysis. Catalysis ; Methanol ; Dimethyl ether ; Crystallinity ; Yield (engineering) ; Fourier transform infrared spectroscopy ; Thermal stability ; Nuclear chemistry ; Adsorption ; Desorption ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies