jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiahao Yan | Han Wei | Han Xie | Xiaokun Gu | Hua Bao
Source: ES Energy & Environments (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Seeking for Low Thermal Conductivity Atomic Configurations in SiGe Alloys with Bayesian Optimization The emergence of data-driven science has opened up new avenues for understanding the thermophysical properties of materials. For decades, alloys are known to possess very low thermal conductivity, but the extreme thermal conductivity that can be achieved by alloying has never been identified. In this work, we combine the Bayesian optimization with a high throughput thermal conductivity calculation to search for the lowest thermal conductivity atomic configuration of SiGe alloy. It is found layered structures are most beneficial for reducing the thermal conductivity among all atomic configurations, which is attributed to the strong branch-folding effect. Furthermore, the roles of interface roughness and layer thicknesses in producing the lowest thermal conductivity are investigated. Through another comprehensive search using Bayesian optimization, the layered structure with smooth interfaces and optimized layer thickness arrangement is eventually identified as the optimal structure with the lowest thermal conductivity. Thermal conductivity ; Materials science ; Conductivity ; Bayesian optimization ; Work (physics) ; Alloy ; Thermal ; Layer (electronics) ; Composite material ; Thermodynamics ; Computer science ; Chemistry ; Physics ; Artificial intelligence ; Physical chemistry Thermal properties of materials ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Luqman S. Maraaba | Khaled Y. Soufi | Luai M. Alhems | Mohamed A. Hassan
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Performance Evaluation of 230 kV Polymer Insulators in the Coastal Area of Saudi Arabia This article investigates the technical performance of 230 kV field-aged composite insulators in the coastal region of Saudi Arabia. Two insulator samples removed from the 230 kV line after 20 years of service are assessed at KFUPM High Voltage Lab (KHVL). Different assessment techniques, such as visual inspection, contact angle, surface pollution severity, electrical withstand, and material characterization are utilized to evaluate the insulator performance. Equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) techniques are used for the surface pollution severity measurements. Clear views of surface changes and surface hydrophobicity conditions are displayed. Scanning electron microscope (SEM), energy dispersive x-ray spectrometry (EDX), and Fourier-transform infrared spectroscopy (FTIR-IR) techniques are utilized for the material characterization. The visual inspection reveals small and big cracks in the insulator sheds. Hard breakable portion areas, many shed cuts, changed color areas, and whitish parts are visually noticed in the insulator surface. Such changes confirm the aging condition effect of the insulator units. Furthermore, the samples are subjected to laboratory clean fog tests to check the electrical performance of the samples. Informative results are given for the current condition and performance for the 20-years, field-aged insulators. Additionally, the experimental results are presented for the rest of the insulator life period. Utilization of the existing line insulators has been evaluated. Finally, discussions and recommendations for the future handling of the insulators at the 230 kV line are highlighted. Insulator (electricity) ; Materials science ; Scanning electron microscope ; Composite material ; Fourier transform infrared spectroscopy ; Environmental science ; Surface conductivity ; Forensic engineering ; Electrical engineering ; Electrical resistivity and conductivity ; Optics ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Aoife Power | Caoimhe Ní Néill | Sive Geoghegan | Sinéad Currivan | Mary Deasy | Daniel Cozzolino
Source: Beverages (article, 2020-08-03)
Field: Chemistry — Analytical Chemistry

A Brief History of Whiskey Adulteration and the Role of Spectroscopy Combined with Chemometrics in the Detection of Modern Whiskey Fraud Food fraud and adulteration is a major concern in terms of economic and public health. Multivariate methods combined with spectroscopic techniques have shown promise as a novel analytical strategy for addressing issues related to food fraud that cannot be solved by the analysis of one variable, particularly in complex matrices such distilled beverages. This review describes and discusses different aspects of whisky production, and recent developments of laboratory, in field and high throughput analysis. In particular, recent applications detailing the use of vibrational spectroscopy techniques combined with data analytical methods used to not only distinguish between brand and origin of whisky but to also detect adulteration are presented. Chemometrics ; Biochemical engineering ; Biotechnology ; Process engineering ; Computer science ; Business ; Engineering ; Chemistry ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Weiwei Jiang | Changhua Lu | Yujun Zhang | Wei Ju | Jizhou Wang | Feng Hong | Tao Wang | Chunsheng Ou
Source: Journal of Spectroscopy (article, 2020-08-03)
Field: Chemistry — Analytical Chemistry

Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy. Variable elimination ; Partial least squares regression ; Algorithm ; Mathematics ; Feature selection ; Wavelength ; Computer science ; Statistics ; Artificial intelligence ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Frederick Lia | Benjamin Vella | Marion Zammit-Mangion | Claude Farrugia
Source: Foods (article, 2020-05-26)
Field: Chemistry — Organic Chemistry

Application of 1H and 13C NMR Fingerprinting as a Tool for the Authentication of Maltese Extra Virgin Olive Oil The application of <sup>1</sup>H and <sup>13</sup>C nuclear magnetic resonance (NMR) in conjunction with chemometric methods was applied for the discrimination and authentication of Maltese extra virgin olive oils (EVOOs). A total of 65 extra virgin olive oil samples, consisting of 30 Maltese and 35 foreign samples, were collected and analysed over four harvest seasons between 2013 and 2016. A preliminary examination of <sup>1</sup>H NMR spectra using unsupervised principle component analysis (PCA) models revealed no significant clustering reflecting the geographical origin. In comparison, PCA carried out on <sup>13</sup>C NMR spectra revealed clustering approximating the geographical origin. The application of supervised methods, namely partial least squares discriminate analysis (PLS-DA) and artificial neural network (ANN), on <sup>1</sup>H and <sup>13</sup>C NMR spectra proved to be effective in discriminating Maltese and non-Maltese EVOO samples. The application of variable selection methods significantly increased the effectiveness of the different classification models. The application of <sup>13</sup>C NMR was found to be more effective in the discrimination of Maltese EVOOs when compared to <sup>1</sup>H NMR. Furthermore, results showed that different <sup>1</sup>H NMR pulse methods can greatly affect the discrimination of EVOOs. In the case of <sup>1</sup>H NMR, the Nuclear Overhauser Effect (NOESY) pulse sequence was more informative when compared to the zg30 pulse sequence, since the latter required extensive spectral manipulation for the models to reach a satisfactory level of discrimination. Proton NMR ; Partial least squares regression ; Cluster analysis ; Carbon-13 NMR ; Maltese ; Pattern recognition (psychology) ; Olive oil ; Pulse sequence ; Artificial intelligence ; Principal component analysis ; Chemistry ; Nuclear magnetic resonance ; Mathematics ; Biological system ; Computer science ; Food science ; Biology ; Statistics ; Physics Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Luxiang Shen | Honghong Wang | Ying Liu | Yang Liu | Xiao Zhang | Yeqi Fei
Source: Applied Sciences (article, 2020-05-29)
Field: Chemistry — Analytical Chemistry

Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder The soluble solids content (SSC) affects the flavor of green plums and is an important parameter during processing. In recent years, the hyperspectral technology has been widely used in the nondestructive testing of fruit ingredients. However, the prediction accuracy of most models can hardly be improved further. The rapid development of deep learning technology has established the foundation for the improvement of building models. A new hyperspectral imaging system aimed at measuring the green plum SSC is developed, and a sparse autoencoder (SAE)–partial least squares regression (PLSR) model is combined to further improve the accuracy of component prediction. The results of the experiment show that the SAE–PLSR model, which has a correlation coefficient of 0.938 and root mean square error of 0.654 for the prediction set, can achieve better performance for the SSC prediction of green plums than the three traditional methods. In this paper, integration approaches have combined three different pretreatment methods with PLSR to predict the SSC in green plums. The SAE–PLSR model has shown good prediction performance, indicating that the proposed SAE–PLSR model can effectively detect the SSC in green plums. Partial least squares regression ; Hyperspectral imaging ; Autoencoder ; Artificial intelligence ; Mean squared error ; Correlation coefficient ; Principal component analysis ; Computer science ; Predictive modelling ; Pattern recognition (psychology) ; Mathematics ; Biological system ; Machine learning ; Statistics ; Deep learning Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Water Quality Monitoring and Analysis
Authors: Manuel David Peris‐Díaz | Roman Guráň | Ondřej Zítka | Vojtěch Adam | Artur Krężel
Source: Journal of Proteome Research (article, 2020-09-14)
Field: Chemistry — Spectroscopy

Mass Spectrometry-Based Structural Analysis of Cysteine-Rich Metal-Binding Sites in Proteins with MetaOdysseus R Software Identification of metal-binding sites in proteins and understanding metal-coupled protein folding mechanisms are aspects of high importance for the structure-to-function relationship. Mass spectrometry (MS) has brought a powerful adjunct perspective to structural biology, obtaining from metal-to-protein stoichiometry to quaternary structure information. Currently, the different experimental and/or instrumental setups usually require the use of multiple data analysis software, and in some cases, they lack some of the main data analysis steps (MS processing, scoring, identification). Here, we present a comprehensive data analysis pipeline that addresses charge-state deconvolution, statistical scoring, and mass assignment for native MS, bottom-up, and native top-down with emphasis on metal–protein complexes. We have evaluated all of the approaches using assemblies of increasing complexity, including free and chemically labeled proteins, from low- to high-resolution MS. In all cases, the results have been compared with common software and proved how MetaOdysseus outperformed them. Mass spectrometry ; Identification (biology) ; Deconvolution ; Pipeline (software) ; Chemistry ; Computer science ; Software ; Data mining ; Folding (DSP implementation) ; Structural biology ; Cysteine ; Computational biology ; Algorithm ; Chromatography ; Biology ; Biochemistry ; Engineering ; Programming language Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Joyjit Chattoraj | Massimo Pica Ciamarra
Source: Physical Review Letters (article, 2020-01-13)
Field: Materials Science — Materials Chemistry

Role of Attractive Forces in the Relaxation Dynamics of Supercooled Liquids The attractive tail of the intermolecular interaction affects very weakly the structural properties of liquids, while it affects dramatically their dynamical ones. Via the numerical simulations of model systems not prone to crystallization, both in three and in two spatial dimensions, here we demonstrate that the nonperturbative dynamical effects of the attractive forces are tantamount to a rescaling of the activation energy by the glass transition temperature T_{g}: systems only differing in their attractive interaction have the same structural and dynamical properties if compared at the same value of T/T_{g}. Supercooling ; Intermolecular force ; Dynamical heterogeneity ; Relaxation (psychology) ; Glass transition ; Physics ; Crystallization ; Chemical physics ; Statistical physics ; Molecular dynamics ; Dynamics (music) ; Materials science ; Thermodynamics ; Condensed matter physics ; Molecule ; Quantum mechanics Material Dynamics and Properties ; Thermodynamic properties of mixtures ; Theoretical and Computational Physics
Authors: Ruggero Giusti | Giovanni Lucchetta
Source: Polymers (article, 2020-09-10)
Field: Materials Science — Polymers and Plastics

Modeling the Adhesion Bonding Strength in Injection Overmolding of Polypropylene Parts In this work, the bonding strength of overmolded polypropylene is investigated and modeled. A T-joint specimen was designed to replicate the bonding between a base and an overmolded stem made of the same polymer: a previously molded plaque was used for the base, and the stem was directly overmolded. The effect of melt temperature, holding pressure, and localized heating was investigated following the design of experiments approach. Both the melt and base temperature positively affect the welding strength. On the contrary, the holding pressure negatively contributed, as the crystallization temperature significantly increases with pressure. Then, the bonding strength of the specimens was predicted using a non-isothermal healing model. Moreover, the quadratic distance of diffusion (based on the self-diffusion model) was calculated and correlated with the bonding strength prediction. The non-isothermal healing model well predicts the bonding strength when the reptation time is calculated within the first 0.09 s of the interface temperature evolution. The prediction error ranges from 1% to 35% for the specimens overmolded at high and low melt and base temperatures, respectively. Materials science ; Reptation ; Composite material ; Polypropylene ; Isothermal process ; Polymer ; Bonding strength ; Diffusion ; Welding ; Adhesion ; Base (topology) ; Thermodynamics Polymer crystallization and properties ; Injection Molding Process and Properties ; Polymer Foaming and Composites
Authors: Afsar Khan | Asif Naeem | Tahira Mahmood
Source: Kinetics and Catalysis (article, 2020-09-01)
Field: Chemistry — Organic Chemistry

Kinetic Studies of Methyl Orange and Congo Red Adsorption and Photocatalytic Degradation onto PVP-Functionalized ZnO This study has focused on the applications of newly prepared zinc oxide (ZnO) composite with polyvinyl pyrrolidone (PVP) for adsorption and photocatalytic removal of methyl orange and congo red. Both the surface area and pore size of ZnO–PVP composite are relatively higher than the simple of unmodified ZnO. This finding signifies the role of PVP in the composites of metal oxide–PVP. The FT-IR analysis showed the chemical interactions of zinc oxide and different functional groups of polyvinyl pyrrolidone, which contribute significantly in removing the azo dyes from the aqueous system. A detailed kinetic study of the sorption used to remove methyl orange and congo red from aqueous solutions by ZnO–PVP composite was undertaken. Pseudo second order kinetic model was found to be best applicable for both the dyes. The mechanism governing the adsorption kinetics was examined by employing the Boyd and intraparticle diffusion models. The results confirmed that the adsorption of dyes by ZnO–PVP composite is mainly controlled by film diffusion (external diffusion) and internal transport of dye is inferior to the external transport. The obtained adsorption capacity for methyl orange is higher than that for congo red due to the presence of an additional functional group and size of the dye. The photocatalytic degradation of dyes with UV light was found to increase significantly with the addition of H2O2. Congo red ; Methyl orange ; Adsorption ; Chemistry ; Photocatalysis ; Aqueous solution ; Zinc ; Oxide ; Composite number ; Sorption ; Chemical engineering ; Orange G ; Inorganic chemistry ; Catalysis ; Nuclear chemistry ; Organic chemistry ; Materials science ; Composite material Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Mouhieddinne Guergueb | Jihed Brahmi | Soumaya Nasri | Frédérique Loiseau | Kaïss Aouadi | Vincent Guérineau | Shabir Najmudin | Habib Nasri
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Zinc(<scp>ii</scp>) triazole <i>meso</i>-arylsubstituted porphyrins for UV-visible chloride and bromide detection. Adsorption and catalytic degradation of malachite green dye Three new triazole <i>meso</i>-arylporphyrins (4a-c) were synthesized by the copper(i)-catalyzed azide alkyne cycloaddition (CuAAC) "click" reaction in high yield. The corresponding zinc(ii) coordination compounds (5a-c) have also been prepared. All 4a-c and 5a-c porphyrin species were fully characterized by elemental analysis, electrospray ionization and MALDI-TOF mass spectrometry, infrared spectroscopy, proton nuclear magnetic resonance, UV-visible, fluorescence and cyclic voltammetry. The zinc(ii) 5a-c complexes have been tested as detectors for Cl<sup>-</sup> and Br<sup>-</sup> anions. UV-visible titrations reveal that these host systems exhibit strong anion binding affinities. The efficiency of the adsorption of the malachite green dye (MG) dye on the 4a-c free base porphyrins and the corresponding zinc(ii) complexes 5a-c was investigated by a kinetic study using these synthetic porphyrin derivatives as adsorbents. The use of our triazole Zn(ii) complexes in the catalytic degradation of the MG dye is the first example where a metalloporphyrin is involved in the MG dye decolorization reaction. The degradation reactions were carried out using an ecological oxidant (H<sub>2</sub>O<sub>2</sub>), where the efficiency of the decolorization has been characterized by UV-visible spectroscopic analysis. Several factors affecting the degradation phenomenon have been studied. The energetic parameters concerning the degradation process have also been determined. Chemistry ; Malachite green ; Zinc ; Catalysis ; Photochemistry ; Adsorption ; Nuclear chemistry ; Azide ; Inorganic chemistry ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Luminescence and Fluorescent Materials ; Heme Oxygenase-1 and Carbon Monoxide
Authors: Thashini Moodley | Moganavelli Singh | R Siegel | K Miller | A Jemal | T Baudino | D Hanahan | R Weinberg | J Padayachee | A Daniels | A Balgobind | M Ariatti | M Singh | V Maney | T Iwamoto | P Sleight | H Pouleur | F Zannad | J Akinyelu | L Mbatha | F Maiyo | N Elahi | M Kamali | M Baghersad | Geetanjali | P Sharma | R Malviya | C Hepokur | I Kariper | S Misir | E Ay | S Tonolu | M Ersez | U Zeybek | S Kuruca | I Yaylim | J Pathak | A Sonker | V Ragneesh; Singh | D Kumar | R Sinha | B Balcomb | S Singh | N Nundkumar | T Moodley | F Tang | L Li | D Chen | S Mngadi | S Mokhosi | N Vallabani | N Hadjesfandiari | A Parambath | P Mishra | B Nayak | R Dey | M Mohammed | J Syeda | K Wasan | E Wasan | T Buranachai | N Praphairaksit | N Muangsin | E Barrett | L Joyner | P Halenda | T Mosmann | V Mosqueira | P Legrand | A Gulik | O Bourdon | R Gref | D Labarre | G Barrat | J Jokerst | T Lobovkina | R Zare | S Gambhir | N Narainpersad
Source: Biointerface Research in Applied Chemistry (article, 2020-12-20)
Field: Materials Science — Biomaterials

Polymeric Mesoporous Silica Nanoparticles for Combination Drug Delivery In vitro Despite the recent advances and development of conventional cancer therapy strategies, treatments often lack specificity, resulting in low therapeutic efficiency, cancer recurrence, and drug resistance. With the advent of nanotechnology, nanoparticle-based delivery systems have steadily gained interest. The key to using any drug delivery system is its’ relative cytotoxicity, pharmacokinetics, and downstream immunological effects that may arise upon repetitive exposure. Among the nanoparticle systems, mesoporous silica nanoparticles (MSNs) have received favorable attention as potential drug delivery platforms. This study aimed to synthesize and functionalized MSNs with chitosan and polyethyleneglycol for improved stability, efficient drug loading, and drug release. These polymerized MSNs were physicochemically and morphologically characterized and assessed for their dual-drug [doxorubicin (DOX)/5-fluoruracil (5-FU)] loading, drug release kinetics, and anticancer activity in vitro. MSNs ranged from 35-70 nm in size, with a high surface area (809.44 m²/g) and a large pore volume (1.74 cm²/g). The DOX/5-FU co-loading produced a potent dual-drug formulation with good pH-responsive release profiles, high percentage release, especially from PEGylated MSNs, and significant anticancer activity the breast adenocarcinoma (MCF-7) and cervical cancer (HeLa) cells. This combination therapy's favorable outcomes suggest an improved therapeutic strategy that warrants investigation in an in vivo model. Drug delivery ; Mesoporous silica ; HeLa ; In vivo ; Drug ; Doxorubicin ; Pharmacology ; Drug carrier ; Chemistry ; Nanoparticle ; Targeted drug delivery ; Cytotoxicity ; Controlled release ; Nanotechnology ; In vitro ; Materials science ; Medicine ; Mesoporous material ; Chemotherapy ; Internal medicine ; Biology ; Biochemistry Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics