jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Padilah wanda Hasibuan | Paris Ali Topan | Desi Maulidyawati | Masyitah Aulia
Source: Journal Altron Journal of Electronics Science & Energy systems (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

ANALISIS PERUBAHAN INTERNAL RESISTANSI PADA KONDISI REST MENGGUNAKAN PULSE TEST DENGAN BEBAN RESISTOR 20 OHM Penelitian ini bertujuan untuk melakukan analisa perubahan internal resistansi pada kondisi rest menggunakan metode pulse test dengan beban resistor 20 ohm. Pulse test adalah teknik pengujian yang digunakan untuk mengukur resistansi internal pada bahan, kondisi rest menunjuk pada keadaan yang telah mengalami proses yang menghasilkan tegangan yang ada di dalam bahan tersebut. Metode pulse test digunakan pada beban resistor 20 ohm yang mengalami kondisi rest untuk menguji perubahan resistansi internal di dalam bahan. Pengujian dilakukan dengan memberikan dorongan listrik berulang pada beban resistor, dan selama pengujian naik turun resistansi internal diukur dan dianalisis. Hasil analisis menunjukkan bahwa pulse test pada kondisi rest mampu memberikan informasi sisa tegangan yang ada didalam bahan dan mengetahui masalah yang terdapat pada struktur komponen yang ada didalam bahan. Penelitian ini memberikan wawasan kualitas dan kekuatan bahan dalam kondisi rest serta membantu dalam perbaikan proses yang membuat pemrosesan untuk menghindari masalah yang terjadi pada saluran resistansi dan pulse test bermanfaat dalam mengevaluasi hasil bahan yang efesien. Kesimpulannya, analisis perubahan internal resistansi pada kondisi rest menggunakan pulse test dengan beban resistor 20 ohm. Physics ; Rest (music) ; Ohm ; Resistor ; Acoustics ; Voltage Material Selection and Properties ; Non-Destructive Testing Techniques
Authors: Nguyn Thnh | Nguyn Bnh | Trng Ton | Nguyn Dng | i Hc Qung Bnh | Vin Nghin | Ht Nhn | Mustafa Soylak | Yunus Unsal | Nebiye Kizil | Ayse Aydin | M Madhava Rao | D Ramana | K Seshaiah | Mc Wang | Chang Sw | Chien | Pan Wang | Mingliang Du | Han Zhu | Shiyong Bao | Tingting Yang | Meiling Zou | Mustafa Imamoglu | Oktay Tekir | Mabrouk Eloussaief | Mourad Benzina | Yongfang Yang | Yulei Xie | Lichuan Pang | Mao Li | Xiaohui Song | Jianguo Wen | Hanying Zhao | Jie Xuemei Ren | Changlun Li | Yang Chen | Diyun Gao | Mianhua Chen | Ahmed Su | Tasawar Alsaedi | Hayat | Benjamin Collins | Brodie | William S Hummers Jr | Richard Offeman | Daniela C Marcano | Jacob Dmitry V Kosynkin | Alexander Berlin | Zhengzong Sinitskii | Alexander Sun | Slesarev | Wei Lawrence B Alemany | James Lu | Tour | Cheera Prasad | Krishna Murthy | Hari Krishna | R Sreenivasa Rao | V Suneetha | Ponneri Venkateswarlu | Zhenghua Wu | Zhimang Gu | Xiaorong Wang | Les Evans | Hongyan Guo | Wan-Chi Tsai | Sonia Ibarra-Buscano | Chi-Chuan Kan | Cybelle Morales Futalan | Maria Lourdes P Dalida | Meng-Wei Wan | Yang Gao | Xuemei Ren | Jianchun Wu | Tasawar Hayat | Ahmed Alsaedi | Cheng Cheng | Changlun Chen | Xue Li | Kang Zhao | Caiyin You | Wensheng Linghu | Feng Ye | Mei Yu | Hui Pan | Jie Luo | Yangjinghua Yu | Zhong Wang | Runjun Sun | Zhihua Chen | Meicheng Liu | Xiang Zhou | Mu Yao | Guohe Wang | Bo Wang | Fan Zhang | Shengfu He
Source: Journal of Science and Technology - HaUI (article, 2023-10-31)
Field: Chemistry — Inorganic Chemistry

Nghiên cứu tổng hợp graphene oxit dạng khử và ứng dụng hấp phụ Pb2+ trong dung dịch nước In the present paper, reduced graphene oxide (rGO) has been synthesized successfully from graphite powder by modified Hummers method. Material were characterized by Fourier transform infrared spectroscopy (FT-IR), Raman spectra and scanning electron microscopy SEM to know the formation of rGO. The nitrogen adsorption-desorption was used for determination of surface area (Brunauer -Emmett -Teller (BET)), porosity and capillary distributionof the fabricated material. The adsorption of Pb 2+ ion on rGO was investigated. The adsorption characteristic and Pb 2+ removal efficiency of the adsorbent have been determined by investigating the influence of operating variables such as dosage of rGO. The maximum Pb 2+ sorption capacity was found to be 37.04 (mg/g) and obtained using 0.30g/50mL rGO when pH equals 5, a temperature of 28C, and contact time as 24 hours. The Langmuir and Freundlich models were used to fit the experimental data and these showed good correlations. Chemistry ; Analytical Chemistry (journal) ; Environmental chemistry Pigment Synthesis and Properties
Authors: Jonathan E. Moussa
Source: arXiv (Cornell University) (preprint, 2023-01-12)
Field: Materials Science — Materials Chemistry

Model selection in atomistic simulation There are many atomistic simulation methods with very different costs, accuracies, transferabilities, and numbers of empirical parameters. I show how statistical model selection can compare these methods fairly, even when they are very different. These comparisons are also useful for developing new methods that balance cost and accuracy. As an example, I build a semiempirical model for hydrogen clusters. Selection (genetic algorithm) ; Computer science ; Model selection ; Statistical physics ; Machine learning ; Physics Machine Learning in Materials Science ; Markov Chains and Monte Carlo Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Pau Besalú‐Sala | Fabien Bruneval | Á. J. Pérez‐Jiménez | J. C. Sancho-Garcı́a | Mauricio Rodríguez‐Mayorga
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

RPA, an accurate and fast method for the computation of static non-linear optical properties The accurate computation of non-linear optical properties (NLOPs) in large polymers requires accounting for electronic correlation effects with a reasonable computational cost. The Random Phase Approximation (RPA) used in the adiabatic connection fluctuation theorem is known to be a reliable and cost-effective method to render electronic correlation effects when combined with the density-fitting techniques and the integration over imaginary frequencies. We explore the ability of the RPA energy expression to predict NLOPs by evaluating RPA electronic energies in the presence of finite electric fields to obtain (using the finite difference method) static polarizabilities and hyper-polarizabilities. We show that RPA based on hybrid functional self-consistent field calculations yields as accurate NLOPs as the best-tuned double-hybrid functionals developed today, with the additional advantage that RPA avoids any system-specific adjustment. Random phase approximation ; Computation ; Adiabatic process ; Statistical physics ; Energy (signal processing) ; Field (mathematics) ; Quadratic equation ; Physics ; Applied mathematics ; Computer science ; Mathematics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum Dots Synthesis And Properties
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2023, 25, 12537 DOI: 10.1039/D3CP90103C This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Computer science ; Library science ; Physics ; Chemistry ; Computer security ; Psychology ; Social psychology Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: Mingdong LI | Joshua Harrington Aheto | Marwan M. A. Rashed | Fangkai Han
Source: Food Science and Technology (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Tracing models for checking beef adulterated with pig blood by Fourier transform near-infrared paired with linear and nonlinear chemometrics Faced with the aggravating issue of meat fraud caused by the addition of low-cost animal blood food, the present work aims to develop FT-NIR-based tracing models for detecting beef adulterated with pig blood. A total of 210 samples were analyzed, including raw beef, beef adulterated with pig blood-based gel, and pure pig blood-based gel prepared. For spectrum denoising, the first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction algorithms were performed and compared. We built, optimized, and compared partial least squares (PLS), support vector machine (SVM), and extreme learning machine (ELM) models for identifying the adulterated beef and predicting adulteration levels. Results indicated that second derivative was the best preprocessed technique for all chemometrics modeling; ELM model achieved the optimal performance when the sin function was used, achieving 100% accuracy for identifying the adulterated beef, and all root mean square errors were less than 0.16% for predicting adulteration levels in training and test sets. These results suggest the optimal ELM models could be employed for rapidly checking beef adulterated with pig blood-based gel using FT-NIR technology. Chemometrics ; Tracing ; Second derivative ; Derivative (finance) ; Mathematics ; Extreme learning machine ; Artificial intelligence ; Partial least squares regression ; Pattern recognition (psychology) ; Food science ; Chromatography ; Computer science ; Chemistry ; Statistics ; Artificial neural network Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Armin Sahinovic | Benjamin Geisler | Rossitza Pentcheva
Source: arXiv (Cornell University) (preprint, 2023-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nature of the magnetic coupling in infinite-layer nickelates versus cuprates In contrast to the cuprates, where the proximity of antiferromagnetism (AFM) and superconductivity is well established, first indications for AFM interactions in superconducting infinite-layer nickelates were only recently obtained. Here, we explore, based on first-principles simulations, the nature of the magnetic coupling in NdNiO2 as a function of the on-site Coulomb and exchange interaction, varying the explicit hole doping and the treatment of the Nd $4f$ electrons. The $U$-$J$ phase diagrams for undoped nickelates and cuprates indicate $G$-type ordering, yet show different $U$ dependency. By either Sr hole doping or explicit treatment of the Nd $4f$ electrons, we find a transition to a Ni $C$-type AFM ground state. We trace the effect of Sr doping back to a distinct accommodation of the holes by the Ni versus Cu $e_g$ orbitals. The interaction between Nd $4f$ and Ni $3d$ states stabilizes $C$-type AFM order on both sublattices. Though spin-orbit interactions induce a band splitting near the Fermi energy, the bad-metal state is retained even under epitaxial strain. These results establish the distinct role of the magnetic interactions in the nickelates versus the cuprates and suggest the former as a unique platform to investigate the relation to unconventional superconductivity. Cuprate ; Condensed matter physics ; Superconductivity ; Antiferromagnetism ; Physics ; Doping ; Atomic orbital ; Electron ; Coulomb ; Coupling (piping) ; Ground state ; Mott insulator ; Materials science ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Nikolas R. Burton | Daniel A. Polasky | Flowreen Shikwanna | Samuel Ofori | Tianyang Yan | Daniel Geiszler | Felipe da Veiga Leprevost | Alexey I. Nesvizhskii | Keriann M. Backus
Source: ChemRxiv (preprint, 2023-03-16)
Field: Chemistry — Organic Chemistry

Enhancing multiplexed cysteine chemoproteomics by uniting FragPipe with solid-phase compatible dialkoxydiphenylsilane reagents The human proteome harbors tens of thousands of ligandable or potentially druggable cysteine residues. Consequently, pinpointing the optimal covalent molecule for each cysteine residue represents an exciting means to close the druggability gap, namely the ~96% of human proteins not yet targeted by an FDA approved drug. Realizing the full therapeutic potential of the cysteineome will require comprehensive proteome-wide cysteine-compound structure activity relationship (SAR) analysis. While mass spectrometry-based chemoproteomic platforms have made significant inroads into this challenge, achieving comprehensive cysteine-SAR necessitates technical innovation in two key areas: (1) streamlined sample preparation workflows and (2) high throughput and high coverage data acquisition. Here we report the silane-based Cleavable Linkers for Isotopically labeled Proteomics (sCLIP) method. sCLIP streamlines sample preparation with unparalleled early-stage isobaric labeling and sample pooling, allowing for high coverage and increased sample throughput via customized low cost 6-plex sample multiplexing. The sCLIP method is distinguished by its unprecedented click-assembled isobaric tags, in which the reporter group is encoded in the sCLIP capture reagent and balancer in the pan cysteine-reactive probe. When paired with a custom FragPipe data analysis workflow and applied to cysteine-reactive fragment screens, sCLIP proteomics revealed established and unprecedented cysteine-ligand pairs, including the discovery that the mitochondrial uncoupling agent FCCP acts as a covalent-reversible cysteine-reactive electrophile. Cysteine ; Druggability ; Proteome ; Proteomics ; Computational biology ; Chemistry ; Small molecule ; Combinatorial chemistry ; Stable isotope labeling by amino acids in cell culture ; Multiplex ; Computer science ; Biochemistry ; Bioinformatics ; Biology Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Advanced Proteomics Techniques and Applications
Authors: Aqsa Arooj | Musammir Khan
Source: Research Square (preprint, 2023-07-17)
Field: Materials Science — Biomaterials

Starch/Pectin as Emerging Renewable Materials for Fabrication of Sustainable Bioplastics for Food Packaging Applications Abstract Currently, due to serious environmental concerns of using non-biodegradable fossil fuel based plastics for food packaging application, the renewable bioplastic obtained from second generation biobased materials (biopolymers &amp; plasticizers) could be an attractive alternative, but fulfilling the consumers demand is a challenging task. Here in this work, starch/pectin as matrix and chitosan co-biopolymers together with tartaric acid (TA)/citric acid (CA) as natural plasticizers were used to fabricate their bioplastics by condensation polymerization accompanied by intermolecular hydrogen bonding interactions. The FTIR analysis confirmed the successful formation of amide/ester bonds and non-covalent hydrogen bonding interactions, while the broad XRD bands indicated the amorphous nature of all the obtained bioplastics. Their swelling ratio ranged between (510–620 ± 50%) and the mass loss (44–76 ± 5%), which was susceptible to acid facilitated hydrolysis for 24 h incubation in water. The obtained tensile modulus was in the range (0.5 ± 0.1 to 7.5 ± 0.5 MPa), which was highest for starch/TA film (7.5 ± 0.5 MPa), attributed to the more plasticization effect in the CA based films. All the samples showed complete degradation (97 ± 2.5%), after 3 days dipping in soil and were ecofriendly by ecotoxicity assay. Therefore, these pure biobased bioplastics could be useful materials for the future food packing industry. Bioplastic ; Plasticizer ; Starch ; Food packaging ; Biopolymer ; Materials science ; Biodegradation ; Pectin ; Polymer ; Organic chemistry ; Polymer chemistry ; Chemistry ; Chemical engineering ; Waste management ; Food science biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Source: International Journal of Corrosion and Scale Inhibition (article, 2023-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermal and corrosion resistance of metal coatings on optical fiber The thermal and corrosion resistance of Ni coating and double-layer Ni-Au coating applied over protective and strengthening copper layer on the optical fiber has been investigated.This subject was considered because the optical fibers are widely used at high temperatures and in aggressive media.The copper layer was deposited from the melt; Ni and Au were deposited electrolytically.The Cu-Ni and Cu-Ni-Au coatings raise the onset temperature for oxidation in air by 200C and reduce markedly the rate of coating oxidation.The thermal treatment of the optical fiber with Cu-Ni and Cu-Ni-Au coatings at temperatures of 300 to 400C brings about small increase in the tensile strength of the fiber.At further rise in the temperature (500C) the tensile strength of the fiber becomes considerably lower, especially for Cu-Ni-Au coatings.The coatings studied pass the humidity chamber test whereas they corrode non-uniformly in the salt spray chamber.After the salt spray test, coating (Ni and Au) delamination was observed in some areas.The corrosion process is probably stimulated by the formation of galvanic couples of copper and nickel or nickel and gold due to the presence of pores and microcracks in the coatings.Nickel coating improves the corrosion resistance in 3% NaCl solution; however, additional Au layer increases the corrosion rate. Corrosion ; Materials science ; Metal ; Metallurgy ; Composite material Copper Interconnects and Reliability ; Optical Coatings and Gratings ; Material Properties and Applications
Authors: María Isabel Sánchez‐Rodríguez | Elena Sánchez‐López | Alberto Marinas | José María Caridad y Ocerín | Francisco J. Urbano
Source: Research Square (preprint, 2023-07-10)
Field: Chemistry — Analytical Chemistry

Enhancing the machine learning classification of olive oils by adding agro-climatic to NIR spectral information Abstract The high-quality indexes of extra virgin olive oil (EVOO) can be affected by frauds in marketing, such as a wrong indication of geographical origin or a false certification label of Protected Designations of Origin (PDO). Therefore, the introduction of sustainable, rapid and cost-effective analytical procedures (such as near infrared spectroscopy, NIR) is essential to authenticate EVOO. As NIR spectra provide non-resolved bands, in contrast to classical techniques (such as chromatography), the application of chemometrics tools is indispensable to extract useful information from spectra. In particular, this work applies principal component analysis (PCA) to reduce the high-dimensional NIR spectral data and then includes these PCA factors as explanatory variables in machine learning models to classify EVOO as a function of the geographical origin or PDO. Furthermore, agro-climatic information is added to the previous classification models, providing a clear increase of the reliability of the estimations in calibration and validation. Consequently, the results emphasize the potential of including agro-climatic information, besides NIR spectral one, in classification models because they substantially optimize the findings obtained from only PCA factors. Principal component analysis ; Chemometrics ; Hyperspectral imaging ; Near-infrared spectroscopy ; Artificial intelligence ; Computer science ; Reliability (semiconductor) ; Pattern recognition (psychology) ; Calibration ; Geographical indication ; Mathematics ; Machine learning ; Statistics ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: N. Kapilan | S Jani | P Yang | S Kokot | A Bledzki | V Sperber | O Faruk | Y Mohammed | Abdella | Y V Ravis
Source: European Chemical Bulletin (article, 2023-05-19)
Field: Materials Science — Polymers and Plastics

ASSESSMENT OF FIRE RESISTANCE CHARACTERISTICS AND THEORETICAL MODELING AND NUMERICAL INVESTIGATION OF EFFECT OF LOAD ON CANTILEVER AND RIGIDLY FIXED NATURAL FIBER REINFORCED GREEN COMPOSITE The current study is ambitious to develop a hybrid composite specimen for evaluating fire resistance characteristics and performing model analysis for validating theoretical and numerical frequencies under cantilever and rigidly fixed support conditions. The composite test specimens are developed using hemp and banana fibers by applying the traditional hand layup fabrication technique. The focus on the use of various natural fibers is very rapid in both domestic and industrial applications. Natural fibers are handy in availability and are possesses extremely good mechanical, chemical and thermal properties. This work is focused on developing the composite specimen according to ASTM –D2863-87, for evaluating fire resistance characteristics. This work also includes theoretical and numerical validation of natural frequency under cantilever and rigidly fixed conditions. The finite Element Analysis (FEM) tool is used to validate the theoretical frequency. Through the statistical analysis, it is concluded that the developed composite specimen showcases good fire resisting property up to a temperature of 175o c with a good fire propagation time of 6minutes. It also exhibits excellent bonding strength up to a temperature of 95o c. It was found that the natural frequency calculated numerically using Roark’s formula closely satisfied with the values of theoretically obtained frequency from the Finite Element Method (FEM). The maximum natural frequency developed under cantilever support is 421.04Hz and 626.82Hz for fixed support conditions. Finite element method ; Cantilever ; Natural frequency ; Structural engineering ; Composite number ; Materials science ; Composite material ; Natural fiber ; Computer simulation ; Numerical analysis ; Fiber ; Fabrication ; Fire resistance ; Vibration ; Engineering ; Acoustics ; Mathematics Natural Fiber Reinforced Composites ; Fire effects on concrete materials
Authors: Serkan ÖRÜCÜ | Süleyman GÖKMEN
Source: International Journal of Advanced Natural Sciences and Engineering Researches (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

MAKİNE ÖĞRENME METOTLARININ MANTI KALİTESİNİN BELİRLENMESİNDE KULLANILABİLİRLİĞİ Geleneksel gıdalardan biri olan ve sevilerek tüketilen mantının, endüstriyel üretimdeki çeşitli süreçlerde karşılaşılabilecek sorunlar nedeniyle kalitesi düşebilmektedir. Kalite kontrolde kullanılan CIE Lab sistemi yani üç nokta yöntemi olarak da bilinen renk sistemine göre mantı üretiminde ürünün kalite düşüşleri belirlenebilmektedir. Bu çalışmada mantı üretiminde ısıl işlem süresine göre oluşturulan mantı veriseti (Mikrodalga seviyesi, L, a, b ve zaman) bazı Makine Öğrenmesi (MÖ) yöntemleri kullanılarak işlenmiştir. Bu işlem sonucunda ise en yüksek f-score’un 87% ile DT (Decision Tree) sınıflandırıcısı ile elde edildiği, bu sonucun ise 73% ile Linear çekirdek kullanılan SVM (Support Vector Machine) ve 71% ile N_neighbours çekirdeği kullanan K-NN (K-Nearest Neighbor) tarafından takip edildiği görülmüştür. Sonuç olarak, MÖ yöntemleri kullanılarak mantı üretim süreçlerinde ürün kalitesi ve güvenilirliğinin arttırılabileceği görülmüştür. Mathematics ; Physics Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods