jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Francisco A. Delesma | Moritz Leucke | Dorothea Golze | Patrick Rinke
Source: arXiv (Cornell University) (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J. Chem. Phys. 150, 174120 (2019)], which preserves the accuracy of the standard global RI method with the Coulomb metric (RI-V) and permits the formulation of cubic-scaling random phase approximation (RPA) and $GW$ approaches. Here, we present the implementation of a separable RI scheme within an all-electron numeric atom-centered orbital framework. We present comprehensive benchmark results using the Thiel and the GW100 test set. Our benchmarks include atomization energies from Hartree-Fock, second-order Møller-Plesset (MP2), coupled-cluster singles and doubles, RPA and renormalized second-order perturbation theory as well as quasiparticle energies from $GW$. We found that the separable RI approach reproduces RI-free HF calculations within 9 meV and MP2 calculations within 1 meV. We have confirmed that the separable RI error is independent of the system size by including disordered carbon clusters up to 116 atoms in our benchmarks Benchmarking ; Atomic orbital ; Resolution (logic) ; Identity (music) ; Separable space ; Atom (system on chip) ; Computer science ; Computational chemistry ; Chemistry ; Mathematics ; Artificial intelligence ; Physics ; Quantum mechanics ; Parallel computing ; Business ; Mathematical analysis ; Electron Catalytic Processes in Materials Science
Authors: Dita Puspitasari | Lia Amelia Tresna Wulan Asri | Nur Fadhilah Syarif
Source: MESIN (article, 2023-12-28)
Field: Materials Science — Biomaterials

Nanucellulose from Tofu Production Waste for Wastewater Treatment Solid waste from tofu production, also known as okara, has numerous applications and can produce high-value-added products. However, the utilization of okara in Indonesia is not yet significant. This written work proposes okara as a source of nanocellulose to produce wastewater adsorbents in freshwater by fabricating it as aerogels and hydrogels. Wastewater is highly hazardous to the environment and living organisms as it can contain saturated salts, heavy metals, organic compounds, oil emulsions, dyes, and even microbes as pollutants that can lead to various diseases or death. Therefore, research on biosorbents is always a hot topic. Biosorption is the process of binding metal ions into the cellular structure of biological materials. Lignocellulosic biosorbents have high adsorption properties due to their ion exchange capability. Okara biosorbent can increase the absorption capacity of Pb2+ ions by up to 20% compared to conventional absorbents. The soybean skin component could also remove contaminated textile dyes from water. Additionally, the low lignin content in okara makes it easier to utilize than other lignocellulosic materials. This research study also shows that okara-based nanocellulose aerogels can maintain their shape or exhibit full shape recovery properties even after being used repeatedly. Nanocellulose ; Wastewater ; Adsorption ; Pulp and paper industry ; Cellulose ; Hazardous waste ; Biosorption ; Materials science ; Ion exchange ; Waste management ; Chemistry ; Chemical engineering ; Organic chemistry ; Ion Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal ; Chemical and Environmental Engineering Research
Authors: Blaine H. M. Mooers
Source: Proceedings of the Python in Science Conferences (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Biomolecular Crystallographic Computing with Jupyter To further advance this use of Jupyter, we developed a collection of code fragments that use the vast Computational Crystallography Toolbox (cctbx) library for novel analyses. We made versions of this library for use in JupyterLab and Colab. Python (programming language) ; Toolbox ; Computer science ; Programming language ; World Wide Web ; Code (set theory) Enzyme Structure and Function ; Genetics, Bioinformatics, and Biomedical Research ; Scientific Computing and Data Management
Authors: Zul Hasrizal Bohari | Muzamir Isa | Faizah Abu Bakar | Ahmad Zaidi Abdullah | Ayob Nazmy Nanyan | Mohamad Na’im Mohd Nasir | Mohamad Fani Sulaima | Emy Zairah Ahmad
Source: Journal of Advanced Research in Fluid Mechanics and Thermal Sciences (review, 2023-07-31)
Field: Materials Science — Materials Chemistry

A Contemporary Review of High Voltage Partial Discharge Detection and Recognition Techniques This review article provides a summary of the most advanced approaches and advancements in the detection and recognition of high voltage partial discharge (PD). It discusses numerous detecting technologies, such as electrical, acoustic, and optical approaches, as well as their merits and disadvantages. It also discusses current developments in signal processing and pattern recognition algorithms used for PD detection and classification. Lastly, the study covers the challenges and limitations in high voltage PD detection and identification studies, as well as potential future solutions. Partial discharge ; Computer science ; Identification (biology) ; High voltage ; Voltage ; Signal processing ; Pattern recognition (psychology) ; Artificial intelligence ; Engineering ; Electrical engineering ; Digital signal processing ; Computer hardware High voltage insulation and dielectric phenomena ; Elevator Systems and Control ; Advanced Computing and Algorithms
Authors: Q. J. Li | Ling Zhao | Xin Yu | Zongbin Liu | Yiqing Zhang
Source: Sensors (article, 2023-08-09)
Field: Chemistry — Analytical Chemistry

An Intelligent Sorting Method of Film in Cotton Combining Hyperspectral Imaging and the AlexNet-PCA Algorithm Long-staple cotton from Xinjiang is renowned for its exceptional quality. However, it is susceptible to contamination with plastic film during mechanical picking. To address the issue of tricky removal of film in seed cotton, a technique based on hyperspectral images and AlexNet-PCA is proposed to identify the colorless and transparent film of the seed cotton. The method consists of black and white correction of hyperspectral images, dimensionality reduction of hyperspectral data, and training and testing of convolutional neural network (CNN) models. The key technique is to find the optimal way to reduce the dimensionality of the hyperspectral data, thus reducing the computational cost. The biggest innovation of the paper is the combination of CNNs and dimensionality reduction methods to achieve high-precision intelligent recognition of transparent plastic films. Experiments with three dimensionality reduction methods and three CNN architectures are conducted to seek the optimal model for plastic film recognition. The results demonstrate that AlexNet-PCA-12 achieves the highest recognition accuracy and cost performance in dimensionality reduction. In the practical application sorting tests, the method proposed in this paper achieved a 97.02% removal rate of plastic film, which provides a modern theoretical model and effective method for high-precision identification of heteropolymers in seed cotton. Hyperspectral imaging ; Dimensionality reduction ; Sorting ; Computer science ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Curse of dimensionality ; Principal component analysis ; Reduction (mathematics) ; Algorithm ; Mathematics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Textile materials and evaluations
Authors: Mr Vineet Agarwal | Shivansh Gupta | Priya Bijay | Shiva M. Singh | S Iqbal | A Gopal | P Sankaranarayanan | D Nair | A Wajid | N Singh | P Junjun | M | V Mohammadi | K Kheiralipour | M Ghasemi-Varnamkhasti | M Dadwal | V Banga | N El-Bendary | E Hariri | A Hassanien | A Badr | P | H | R Krishnan | S Mohd Radzi | M Shah Rizam | A Farah Yasmin | M Ahmad Ihsan | K Shazana | S Khirade | A Patil | A Awate | D Deshmankar | G Amrutkar | U Bagul | S Sonavane | C Nandi | B Tudu | C Koley | S Dubey | M Bhange | H Hingoliwala | J Pujari | R Yakkundimath | A Byadgi | P Singh | Varsha Yadav | A Misra
Source: International Research Journal of Modernization in Engineering Technology and Science (article, 2023-05-31)
Field: Chemistry — Analytical Chemistry

FRUIT QUALITY INSPECTION AND SELLING SYSTEM USING ARTIFICIAL NEURAL NETWORK ALGORITHM The financial development of our nation is mainly dependent upon agriculture. Fruit production with high outputs and rapid production growth are important to the agriculture sector. The degraded quality of crop, fruit, and vegies is one of the consequence of insufficient technological development. This is due to the fact that only outward characteristics of the products' quality-such as their appearance, structure, color, and textureare considered, and these aspects are subject to human mistake.. Application of image processing has improved fruit sorting, disease detection, irrigation, and maturity grading in agriculture. Techniques for image processing can be utilized to cut down on the amount of time needed and lower the cost. Using image processing techniques, we have offered a survey in this study to address these issues. Artificial neural network ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Algorithm Spectroscopy and Chemometric Analyses
Authors: Pradeep Sharma | Rajpal Singh | Manoj Kumar Pathak | Vishal Saxena
Source: Journal of Analysis and Computations (article, 2023-06-01)
Field: Materials Science — General Materials Science

ANN APPROACH BASED STUDY TO PREDICT THE MECHANICAL PROPERTIES OF COPPER POWDER FILLED LLDPE COMPOSITES The effective mechanical properties of copper powder filled with linear low-density polyethylene (LLDPE) are studied by using artificial neural network (ANN) approach. It is a form of artificial intelligence, which deal with the function of human brain and nervous system. ANN technique is more important in many fields of engineering and research applications. This report presents the use of ANN technique for the accurate prediction of mechanical properties of copper powder filled LLDPE composites. ANN is based on Feed Forward Back Propagation (FFBP) using three different training functions (TRAINGDA, TRAINGDM, and TRAINGDX). The input parameters manipulated for prediction are Elongation at break, Stress at break, Young's modulus, volume fraction of the filler and many types of K constants. Copper filled with LLDPE have complex structure which is difficult to predict mechanical properties accurately. This prediction is done using ANN approach. Theoretical model is being compared with experimental data and found that comparison is in good agreement. Copper ; Materials science ; Linear low-density polyethylene ; Composite material ; Metallurgy ; Polymer Material Properties and Applications ; Structural Analysis and Optimization
Authors: Serdar ÖDEV | Bahar Uymaz
Source: Journal of Balkan Science and Technology (article, 2023-08-31)
Field: Materials Science — Polymers and Plastics

An Approach to Spunlace Fabrics: A Prediction on the Tensile Strength Using Artificial Neural Network and the Improvement of the Production Quality Using Fuzzy-Logic Controller Artificial neural network ; Fuzzy logic ; Ultimate tensile strength ; Controller (irrigation) ; Quality (philosophy) ; Computer science ; Production (economics) ; Artificial intelligence ; Control theory (sociology) ; Engineering ; Control (management) ; Materials science ; Composite material ; Economics ; Biology Textile materials and evaluations
Authors: Paul Chen | Pavan Manjunath | Sasindu Wijeratne | Bingyi Zhang | Viktor K. Prasanna
Source: arXiv (Cornell University) (preprint, 2023-08-04)
Field: Materials Science — Materials Chemistry

Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and intermediate data in GNN computations. For dynamic sparsity exploitation, we leverage the heterogeneous computing capabilities of AMD Versal ACAP architecture to accelerate GNN inference. We develop a custom hardware module that executes the sparse primitives of the computation kernel on the Programmable Logic (PL) and efficiently computes the dense primitives using the AI Engine (AIE). To exploit data sparsity during inference, we devise a runtime kernel mapping strategy that dynamically assigns computation tasks to the PL and AIE based on data sparsity. Our implementation on the VCK5000 ACAP platform leads to superior performance compared with the state-of-the-art implementations on CPU, GPU, ACAP, and other custom GNN accelerators. Compared with these implementations, we achieve significant average runtime speedup across various models and datasets of 162.42x, 17.01x, 9.90x, and 27.23x, respectively. Furthermore, for Graph Convolutional Network (GCN) inference, our approach leads to a speedup of 3.9-96.7x compared to designs using PL only on the same ACAP device. Speedup ; Computer science ; Inference ; Computation ; Leverage (statistics) ; Exploit ; Kernel (algebra) ; Graph ; Parallel computing ; Hardware acceleration ; Theoretical computer science ; Computer engineering ; Field-programmable gate array ; Algorithm ; Artificial intelligence ; Embedded system Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Memory and Neural Computing
Authors: Joshua D. Elliott | Mara Chiricotto | Alessandro Troisi | Paola Carbone
Source: Carbon (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Do Specific Ion Effects Influence the Physical Chemistry of Aqueous Graphene-Based Supercapacitors? Perspectives from Multiscale Qmmd Simulations Whether or not specific ion effects determine the charge storage properties of aqueous graphene and graphite-based supercapacitors remains a highly debated topic. In this work we present a multiscale quantum mechanics – classical molecular dynamics (QMMD) investigation of aqueous mono- and divalent salt electrolytes in contact with fully polarizable charged graphene sheets. By computing both the electrochemical double layer (EDL) and quantum capacitance we observe a constant electrode specific capacitance with cationic radii and charge. Counterintuitively, we determine that a switch in the cation adsorption mechanism from inner to outer Helmholtz layers leads to negligible changes to the EDL capacitance, this appears to be due to the robust electronic structure of the graphene electrodes. However, the ability of ions (such as K+) with a relatively low hydration free energy to penetrate the inner Helmholtz plane and adsorb directly on the electrode surface is found to slow their diffusion parallel to the interface. Ions in the outer Helmholtz layer are found to have higher diffusivity at the surface due to their position in ion channels between water layers. Our results show that surface effects such as the surface polarization and the partial dehydration and local structuring of ions on the surface underpin the behaviour of cations at the interface and add a vital new perspective on trends in ion mobilities seen under confinement. Graphene ; Chemical physics ; Supercapacitor ; Capacitance ; Adsorption ; Molecular dynamics ; Aqueous solution ; Ion ; Electrolyte ; Chemistry ; Materials science ; Electrode ; Nanotechnology ; Computational chemistry ; Physical chemistry Electrostatics and Colloid Interactions ; Supercapacitor Materials and Fabrication ; Nanopore and Nanochannel Transport Studies
Authors: Maoliang Wei | Kai Xu | Bo Tang | Junying Li | Yiting Yun | Peng Zhang | Yingchun Wu | Kangjian Bao | Kunhao Lei | Zequn Chen | Hui Ma | Chunlei Sun | Ruonan Liu | Ming Li | Lan Li | Hongtao Lin
Source: arXiv (Cornell University) (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

"Zero change" platform for monolithic back-end-of-line integration of phase change materials in silicon photonics Monolithic integration of novel materials for unprecedented device functions without modifying the existing photonic component library is the key to advancing heterogeneous silicon photonic integrated circuits. To achieve this, the introduction of a silicon nitride etching stop layer at selective area, coupled with low-loss oxide trench to waveguide surface, enables the incorporation of various functional materials without disrupting the reliability of foundry-verified devices. As an illustration, two distinct chalcogenide phase change materials (PCM) with remarkable nonvolatile modulation capabilities, namely Sb2Se3 and Ge2Sb2Se4Te1, were monolithic back-end-of-line integrated into silicon photonics. The PCM enables compact phase and intensity tuning units with zero-static power consumption. Taking advantage of these building blocks, the phase error of a push-pull Mach-Zehnder interferometer optical switch could be trimmed by a nonvolatile phase shifter with a 48% peak power consumption reduction. Mirco-ring filters with a rejection ratio &gt;25dB could be applied for &gt;5-bit wavelength selective intensity modulation, and waveguide-based &gt;7-bit intensity-modulation photonic attenuators could achieve &gt;39dB broadband attenuation. The advanced "Zero change" back-end-of-line integration platform could not only facilitate the integration of PCMs for integrated reconfigurable photonics but also open up the possibilities for integrating other excellent optoelectronic materials in the future silicon photonic process design kits. Photonics ; Silicon photonics ; Materials science ; Optoelectronics ; Waveguide ; Back end of line ; Photonic integrated circuit ; Phase shift module ; Modulation (music) ; Insertion loss ; Electronic engineering ; Computer science ; Optics ; Engineering ; Physics Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Optical Network Technologies
Authors: Sangwon Oh | Seong-Joo Lee | Jeong Hyun Shim | Nam Woong Song | Trương Thị Hiên
Source: arXiv (Cornell University) (preprint, 2023-01-14)
Field: Materials Science — Materials Chemistry

Quantum diamond microscopy with optimized magnetic field sensitivity and sub-ms temporal resolution Quantum diamond magnetometers using lock-in detection have successfully detected weak bio-magnetic fields from neurons, a live mammalian muscle, and a live mouse heart. This opens up the possibility of quantum diamond magnetometers visualizing microscopic distributions of the bio-magnetic fields. Here, we demonstrate a lock-in-based wide-field quantum diamond microscopy, achieving a mean volume-normalized per pixel sensitivity of 43.9 $\mathrm{nTμm^{1.5}/Hz^{0.5}}$. We optimize the sensitivity by implementing a double resonance with hyperfine driving and magnetic field alignment along the $&lt;$001$&gt;$ orientation of the diamond. Additionally, we show that sub-ms temporal resolution ($\sim$ 0.4 ms) can be achieved while keeping the per-pixel sensitivity at a few tens of nanotesla per second using quantum diamond microscopy. This lock-in-based diamond quantum microscopy could be a step forward in mapping functional activity in neuronal networks in micrometer spatial resolution. Magnetometer ; Diamond ; Microscopy ; Magnetic field ; Magnetic resonance force microscopy ; Resolution (logic) ; Sensitivity (control systems) ; Physics ; Quantum ; Materials science ; Magnetic force microscope ; Optical microscope ; Magnetic resonance microscopy ; Temporal resolution ; Nuclear magnetic resonance ; Optics ; Magnetic resonance imaging ; Spin echo ; Ferromagnetic resonance ; Quantum mechanics ; Computer science ; Magnetization Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Takato Kimoto | M. Hasegawa | Takuya Okada | Naoki Toyooka | Shigeki Hirobayashi
Source: Research Square (preprint, 2023-05-02)
Field: Chemistry — Analytical Chemistry

Highly accurate peak detection for low-molecular compounds using NHA with truncation of 1H-13C HMBC spectral data Truncation (statistics) ; Chemistry ; Biological system ; Materials science ; Mathematics ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: A. M. Bohdan | Yanina Romanenko | Mikhail Zavhorodnii | Maryna N. Kornet | V. I. Shupeniuk | Nepolraj Amaladoss | E. M. Klimova | Oleksandr Brazhko
Source: Chemistry & Chemical Technology (article, 2023-12-25)
Field: Chemistry — Organic Chemistry

Design, Synthesis and Biological Activity of the 4-Thioquinoline Derivative One of the promising areas in the creation of bioregulators is the modeling of compounds that combine several pharmacophores. The design of new highly efficient and low-toxic cytoprotectors is largely based on the derivatives of nitrogen-containing heterocycles, and quinoline plays a significant role among these compounds. The researchers evaluated the toxicity of the tested compounds in silico, in vitro, and in vivo, which allowed determiningseveral factors that affect the level of toxic action of 4-thioquinoline derivatives and the direction of non-toxic substances in this sequence. The studied 4-thioquinolines showed a moderate antiradical action in the experiment, inferior to the reference antioxidant Acetylcysteine. The most active compounds are 7-chloro-4-thioquinoline derivatives with propanoic acid residues in the 4th position. 2-(7-chloroquinolin-4-ylthio)propanoic acid and sodium salt of 2-amino-3-((7-chloroquinolin-4-yl)thio)propanoic acid showed the most promising results and their antioxidant action was higher than Tiotriazolin (the comparator) by 27 % and 41 %, respectively. The studied compounds showed a protective effect under H2O2-induced oxidative stress against male sperm according to the main indicators of sperm fertility. It was found that the compounds withresidues of succinic acid, cysteamine, or cysteine in the molecule structure are not inferior to reference drugs. On average, 2-((7-chloroquinolin-4-yl)thio)succinic acid and 2-((quinolin-4-yl)thio)ethanaminedihydrochloride exceeded the comparison drug Acetylcysteine and were on a par with the effect of Ascorbic acid. Chemistry ; Propanoic acid ; Thio- ; Stereochemistry ; Ascorbic acid ; Antioxidant ; Cysteine ; Succinic acid ; Biochemistry ; Enzyme Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactions of Organic Compounds