jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Uzair Mohammad Kakar | Mohammad Hassan Hassand | Atiqullah Sarwari | Muhammad Hashimi
Source: Bulletin of Toraighyrov University Chemistry & Biology series (article, 2023-12-28)
Field: Chemistry — Organic Chemistry

ADSORPTION OF 2,4-DICHLOROPHENOXY ACETIC ACID FROM WATER BY ACTIVATED CARBON The persistent herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) poses a greater risk to the contamination of water bodies, thus asking for other means of reclaiming water and reuse. The conventional treatment processes, however, are generally not capable of removing this contaminant, which makes scientists search for ways to replace them. This study goes deeper into the use of activated carbon as a powerful yet all-surfacing adsorbent with tremendous abilities to eliminate 2,4-D from water. The chemical and physical characteristics of 2,4-D, such as their incompatibility with endosulfan, instability, and hydrophobicity, are some aspects that make purification difficult by the traditional purification method. Activated carbon, retrieved from materials such as the shells of coconuts and even wood, is among the best options with its porosity, as it has an endless surface area for adsorption, thereby increasing the rate of absorption of 2,4-D. Parameters that determine the adsorption behavior, inclusive of pH, temperature, and initial concentration, are discussed in this study to understand how water pollution can be conducted. The study focuses on activated carbon species, in particular powdered, granular, and activated carbon fibers with unique advantages in achieving 2,4-D removal. There are two methods, intrinsic and extrinsic, that can be used to trigger the surface, and the physicochemical action consists of hydrogen bonding between the activated carbon as an adsorbent and cellulose, which is the main determinant of surface activation. Both molecules interact, as is proven in the application of isotherm models such as Langmuir and Freundlich types, which lead some research reports to indicate challenges and future directions requiring refined purification processes, involving AI in predictive modeling, and interdisciplinarity in addressing 2,4-D contamination in surface waters. Keywords: Activated carbon, 2,4-dichlorophenoxy acetic acid, adsorption, environmental pollution, water contamination. Activated carbon ; Adsorption ; Acetic acid ; Chemistry ; Chemical engineering ; Organic chemistry ; Engineering Inorganic and Organometallic Chemistry ; Water Quality Monitoring and Analysis
Authors: Scott E. Campbell | G. Bollen | Alec Hamaker | Walter Kretzer | R. Ringle | S. Schwarz
Source: Atoms (article, 2023-09-28)
Field: Chemistry — Spectroscopy

Applications of Machine Learning and Neural Networks for FT-ICR Mass Measurements with SIPT The single-ion Penning trap (SIPT) at the Low-Energy Beam Ion Trapping Facility has been developed to perform precision Penning trap mass measurements of single ions, ideal for the study of exotic nuclei available only at low rates at the Facility for Rare Isotope Beams (FRIB). Single-ion signals are very weak—especially if the ion is singly charged—and the few meaningful ion signals must be disentangled from an often larger noise background. A useful approach for simulating Fourier transform ion cyclotron resonance signals is outlined and shown to be equivalent to the established yet computationally intense method. Applications of supervised machine learning algorithms for classifying background signals are discussed, and their accuracies are shown to be ≈65% for the weakest signals of interest to SIPT. Additionally, a deep neural network capable of accurately predicting important characteristics of the ions observed by their image charge signal is discussed. Signal classification on an experimental noise dataset was shown to have a false-positive classification rate of 10.5%, and 3.5% following additional filtering. The application of the deep neural network to an experimental 85Rb+ dataset is presented, suggesting that SIPT is sensitive to single-ion signals. Lastly, the implications for future experiments are discussed. Penning trap ; Ion ; Fourier transform ion cyclotron resonance ; Ion trap ; Artificial neural network ; Noise (video) ; Physics ; Computer science ; Artificial intelligence ; SIGNAL (programming language) ; Energy (signal processing) ; Pattern recognition (psychology) ; Atomic physics ; Computational physics Mass Spectrometry Techniques and Applications ; Nuclear physics research studies
Authors: Laureen Coïc | Raffaele Vitale | Myriam Moreau | David Rousseau | José Henrique de Morais Goulart | Nicolas Dobigeon | Cyril Ruckebusch
Source: ChemRxiv (preprint, 2023-04-02)
Field: Chemistry — Analytical Chemistry

Assessment of essential information in the Fourier domain to accelerate Raman hyperspectral microimaging In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows or/and columns of a two-way data matrix. These rows/columns are called essential because they are indispensable to reproduce the full data matrix in a convex linear way. The selection of EI is driven by the properties of linear spectral mixtures. In recent works, it has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on principal component analysis (PCA) to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach, denominated EIFD, which consists in extracting EI in the Fourier domain. Spectral information is transformed into Fourier coefficients and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of the first few harmonics. The proposed EIFD approach is broadly applicable and very efficient in terms of spectral compression and denoising. In particular, it has the crucial advantage that the coordinate system of a phasor plot does not depend on the data themselves. This enables dynamic (i.e., performed during data acquisition) and chemometric-driven (i.e., based on spectral relevance for unmixing) selective sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating the possibility of a 50-fold acceleration of Raman acquisition. Hyperspectral imaging ; Fourier transform ; Context (archaeology) ; Computer science ; Principal component analysis ; Convex hull ; Algorithm ; Spectral resolution ; Mathematics ; Artificial intelligence ; Regular polygon ; Physics ; Spectral line ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Laureen Coïc | Raffaele Vitale | Myriam Moreau | D. Rousseau | José Henrique de Morais Goulart | Nicolas Dobigeon | Cyril Ruckebusch
Source: ChemRxiv (preprint, 2023-07-07)
Field: Chemistry — Analytical Chemistry

Assessment of essential information in the Fourier domain to accelerate Raman hyperspectral microimaging In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows or/and columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on principal component analysis (PCA) to evaluate the convex hull that encapsulates the data structure in the normalized scores space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition. Hyperspectral imaging ; Principal component analysis ; Fourier transform ; Computer science ; Phasor ; Context (archaeology) ; Raman spectroscopy ; Convex hull ; Partial least squares regression ; Artificial intelligence ; Mathematics ; Optics ; Physics ; Regular polygon Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Imaging and Spectroscopy Techniques
Authors: Dr.R. Anbazhagan Dr.R. Suresh
Source: International Journal for Modern Trends in Science and Technology (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Experimental Analysis of Thermoelectric Generation using Different Modes The waste heat from energy company consumption sectors, when rejected into atmosphere, are useless and it contributes to global warming. Nowadays industrial activities and energy sectors (power stations, oil refineries, coke ovens, etc.) are the most energy consuming sectors worldwide and, consequently, the responsible for the release of large quantities of industrial waste heat to the environment by means of hot exhaust gases, cooling media and heat lost from hot equipment surfaces and heated products. Recovering and reusing these waste heats would provide an attractive opportunity for a low-carbon and less costly energy resource. Moreover, reducing the environmental impact. Thermoelectric generator is the one of the methods which helps to recover this waste heat, designing of thermoelectric generator was based on the range of temperature produced in industries and the objective is to generate optimum power with optimum material, COMSOL Multiphysics software is the tool, which has been used to get the simulation results. High manganese silicide (HMS) has been chosen according to the properties of waste heat from industries, the simulation results shows that thermoelectric generator can be a good way to recover waste heat from local industries and converted to useful power, for instance to supply small sensing electronic equipment in the plant Waste heat ; Thermoelectric generator ; Waste management ; Process engineering ; Waste heat recovery unit ; Reuse ; Environmental science ; Multiphysics ; Thermoelectric effect ; Electricity generation ; Heat exchanger ; Engineering ; Mechanical engineering ; Power (physics) ; Finite element method Advanced Thermoelectric Materials and Devices
Authors: Mohsin Jahan Qazi | Nate Cira
Source: Biophysical Journal (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Crystallization by single diffusion through agar gel Crystallization ; Supersaturation ; Nucleation ; Solubility ; Diffusion ; Solvent ; Chemistry ; Chemical physics ; Chemical engineering ; Thermodynamics ; Materials science ; Physical chemistry ; Organic chemistry ; Physics Crystallization and Solubility Studies
Authors: Ran Gu | Yevgeny Rakita | Ling Lan | Zach Thatcher | Gabrielle E. Kamm | Daniel O’Nolan | Brennan C. McBride | Allison Wustrow | James R. Neilson | Karena W. Chapman | Qiang Du | Simon J. L. Billinge
Source: arXiv (Cornell University) (preprint, 2023-11-26)
Field: Materials Science — Materials Chemistry

Stretched Non-negative Matrix Factorization An algorithm is described and tested that carries out a non negative matrix factorization (NMF) ignoring any stretching of the signal along the axis of the independent variable. This extended NMF model is called StretchedNMF. Variability in a set of signals due to this stretching is then ignored in the decomposition. This can be used, for example, to study sets of powder diffraction data collected at different temperatures where the materials are undergoing thermal expansion. It gives a more meaningful decomposition in this case where the component signals resemble signals from chemical components in the sample. The StretchedNMF model introduces a new variable, the stretching factor, to describe any expansion of the signal. To solve StretchedNMF, we discretize it and employ Block Coordinate Descent framework algorithms. The initial experimental results indicate that StretchedNMF model outperforms the conventional NMF for sets of data with such an expansion. A further enhancement to StretchedNMF for the case of powder diffraction data from crystalline materials called Sparse-StretchedNMF, which makes use of the sparsity of the powder diffraction signals, allows correct extractions even for very small stretches where StretchedNMF struggles. As well as demonstrating the model performance on simulated PXRD patterns and atomic pair distribution functions (PDFs), it also proved successful when applied to real data taken from an in situ chemical reaction experiment. Non-negative matrix factorization ; Diffraction ; Matrix decomposition ; Discretization ; Factorization ; Algorithm ; Coordinate descent ; Matrix (chemical analysis) ; Powder diffraction ; Gradient descent ; Decomposition ; Variable (mathematics) ; Computer science ; Set (abstract data type) ; Materials science ; Mathematics ; Mathematical analysis ; Physics ; Chemistry ; Optics ; Artificial intelligence ; Crystallography ; Composite material X-ray Diffraction in Crystallography
Authors: Shamik Chanda | Sangita Sen
Source: arXiv (Cornell University) (preprint, 2023-11-29)
Field: Chemistry — Physical and Theoretical Chemistry

Benchmark Computations of Nearly Degenerate Singlet and Triplet states of N-heterocyclic Chromophores : I. Wavefunction-based Methods In this paper we investigate the role of electron correlation in predicting the S$_1$-S$_0$ and T$_1$-S$_0$ excitation energies and hence, the singlet-triplet gap ($Δ$E$_{ST}$) in a set of cyclazines which act as templates for potential candidates for 5th generation Organic Light Emitting Diode (OLED) materials. This issue has recently garnered much interest with the focus being on the inversion of the $Δ$E$_{ST}$, although experiments have indicated near degenerate levels with both positive and negative being within the experimental error bar (\emph{J. Am. Chem. Soc. 1980, 102: 6068 , J. Am. Chem. Soc. 1986, 108: 17} ). We have carried out a systematic and exhaustive study of various excited state electronic structure methodologies and identified the strengths and shortcomings of the various approaches and approximations in view of this challenging case. We have found that near degeneracy can be achieved either with a proper balance of static and dynamic correlation in multireference theories or with state-specific orbital corrections including its coupling with correlation. The role of spin contamination is also discussed. Eventually, this paper seeks to produce benchmark numbers for establishing cheaper theories which can then be used for screening derivatives of these templates with desirable optical and structural properties. Additionally we would like to point out that the use of DLPNO-STEOM-CCSD as the benchmark for $Δ$E$_{ST}$ (as used in \emph{J. Phys. Chem. A 2022, 126: 8: 1378, Chem. Phys. Lett. 2021, 779: 138827}) is not a suitable benchmark for this class of molecules. Singlet state ; Degenerate energy levels ; Excited state ; Excitation ; OLED ; Degeneracy (biology) ; Benchmark (surveying) ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics ; Materials science ; Nanotechnology ; Bioinformatics Photochemistry and Electron Transfer Studies ; Molecular Junctions and Nanostructures ; Molecular spectroscopy and chirality
Authors: Ali Abou Taka | John M. Herbert | Laura M. McCaslin
Source: ChemRxiv (preprint, 2023-10-08)
Field: Chemistry — Physical and Theoretical Chemistry

Ground State Orbital Analysis Predicts S1 Charge Transfer in Donor–Acceptor Materials Donor-acceptor (D-A) materials, such as D-A co- crystals and D-A copolymers, can exhibit a wide range of unique photophysical properties with applications in next-generation optoelectronics. The properties of D-A dimer models, computed with electronic structure calculations, are often employed to predict properties of D-A materials. One of the most important D-A dimer quantities is the degree of charge transfer (DCT) in the S1 state, which correlates with properties such as fluorescence lifetimes and intersystem crossing rates. Predictive metrics of the S1 DCT generally require an excited state quantum chemistry calculation. Presented here is a novel metric for predicting the degree of charge transfer (DCT) in the S1 electronic state of D-A materials, computed solely with ground state orbital analysis. This metric computes the average of two quantities: (1) the degree of similarity between the highest occupied molecular orbital (HOMO) in the donor molecule and the D-A complex and (2) the degree of similarity between the lowest occupied molecular orbital (LUMO) in the acceptor molecule and the D-A complex. A linear relationship between this similarity metric and the DCT in the S1 state (HOMO → LUMO transition) is demonstrated for a data set of 31 D-A dimers. The integration of this novel orbital structure- function relationship into high-throughput screening methods is discussed alongside best practices for choosing molecular geometries and quantifying the DCT. HOMO/LUMO ; Metric (unit) ; Intersystem crossing ; Molecular orbital ; Acceptor ; Ground state ; Charge (physics) ; Excited state ; Similarity (geometry) ; Chemistry ; Molecule ; Electronic structure ; Materials science ; Physics ; Chemical physics ; Molecular physics ; Computational chemistry ; Atomic physics ; Computer science ; Quantum mechanics ; Singlet state ; Artificial intelligence Photochemistry and Electron Transfer Studies ; Organic Light-Emitting Diodes Research ; Luminescence and Fluorescent Materials
Authors: Devanshu Agrawal | James Ostrowski
Source: arXiv (Cornell University) (preprint, 2023-03-08)
Field: Materials Science — Materials Chemistry

Densely Connected $G$-invariant Deep Neural Networks with Signed Permutation Representations We introduce and investigate, for finite groups $G$, $G$-invariant deep neural network ($G$-DNN) architectures with ReLU activation that are densely connected-- i.e., include all possible skip connections. In contrast to other $G$-invariant architectures in the literature, the preactivations of the$G$-DNNs presented here are able to transform by \emph{signed} permutation representations (signed perm-reps) of $G$. Moreover, the individual layers of the $G$-DNNs are not required to be $G$-equivariant; instead, the preactivations are constrained to be $G$-equivariant functions of the network input in a way that couples weights across all layers. The result is a richer family of $G$-invariant architectures never seen previously. We derive an efficient implementation of $G$-DNNs after a reparameterization of weights, as well as necessary and sufficient conditions for an architecture to be ``admissible''-- i.e., nondegenerate and inequivalent to smaller architectures. We include code that allows a user to build a $G$-DNN interactively layer-by-layer, with the final architecture guaranteed to be admissible. We show that there are far more admissible $G$-DNN architectures than those accessible with the ``concatenated ReLU'' activation function from the literature. Finally, we apply $G$-DNNs to two example problems -- (1) multiplication in $\{-1, 1\}$ (with theoretical guarantees) and (2) 3D object classification -- % finding that the inclusion of signed perm-reps significantly boosts predictive performance compared to baselines with only ordinary (i.e., unsigned) perm-reps. Invariant (physics) ; Equivariant map ; Permutation (music) ; Deep neural networks ; Computer science ; Artificial neural network ; Architecture ; Discrete mathematics ; Mathematics ; Theoretical computer science ; Pure mathematics ; Artificial intelligence Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Neural Network Applications
Authors: Ming‐Yuan Cheng | Linzhou Zhang
Source: Energies (article, 2023-03-24)
Field: Chemistry — Analytical Chemistry

Molecular Reconstruction Method Based on NIR Spectroscopy for Reformates With the increasing attention to environmental protection and strict national standards, the production of high-quality clean gasoline is more and more required in modern refineries. At present, the molecular-level online modeling of the gasoline blending process is rarely reported due to the lack of a proper characterization method. In this study, we have developed a molecular reconstruction method based on near-infrared (NIR) spectroscopy. We established a library that comprises an NIR spectrum and molecular composition of known reformates. We developed the sample selection algorithm that finds the sample with an NIR spectrum close to the target sample. The method-solving algorithm based on multiple linear regression was used to obtain the molecular composition of gasoline quickly. The method combines online measurement methods and molecular management technology to achieve online composition reconstruction. Seventy-five groups of reformates were collected to verify the feasibility of the method, and the results showed that the method predicted well. Gasoline ; Sample (material) ; Near-infrared spectroscopy ; Molecular spectroscopy ; Process engineering ; Spectroscopy ; Oil refinery ; Computer science ; Process (computing) ; Materials science ; Chemistry ; Engineering ; Optics ; Chromatography ; Waste management ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ubaid Ullah | Ali Hussain | Usman Ali | Mohabbat ali
Source: Journal of advancement in computing. (article, 2023-12-31)
Field: Chemistry — Analytical Chemistry

ORANGE FRUIT IMAGE QUALITY ASSESSMENT USING HYBRID MODEL Since ripeness is regarded by consumers as the most important quality indication in the crops industry, maintaining and monitoring its ripeness has become a major issue in the business of growing crops. Additionally, as it significantly affects the product's quality and consumer preferences, the product's appearance is one of the makers' most pressing worries. However, the forecasting of storage life and the selection of the best harvest dates are still largely based on individual interpretation and practical experience. Threshold segmentation and certain morphological techniques are used in the early stage of the extraction of an area of interest. The segmented orange images are then divided into training and testing data sets, and the second stage entails extracting color-based traits from them. Choosing the classifier training settings is the focus of the third phase. The final phase, which makes use of the previously trained ANN, categorizes the data. One of the most crucial conclusions of this study is that creating a neural network is an empirical process that requires a lot of trials in order to identify the ideal variables that will give the neural classification algorithm the best performance. initialization of weights, the quantity of hidden neurons. Based on the proposed model the accuracy and efficiency is 1.3% which is more accurate result. Orange (colour) ; Computer science ; Image quality ; Artificial intelligence ; Quality (philosophy) ; Horticulture ; Computer vision ; Agricultural engineering ; Image (mathematics) ; Biology ; Engineering ; Physics Spectroscopy and Chemometric Analyses