jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H. J. Jonas | Simon Stuij | Peter Schall | Peter G. Bolhuis
Source: The Journal of Chemical Physics (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

A temperature-dependent critical Casimir patchy particle model benchmarked onto experiment Synthetic colloidal patchy particles immersed in a binary liquid mixture can self-assemble via critical Casimir interactions into various superstructures, such as chains and networks. Up to now, there are no quantitatively accurate potential models that can simulate and predict this experimentally observed behavior precisely. Here, we develop a protocol to establish such a model based on a combination of theoretical Casimir potentials and angular switching functions. Using Monte Carlo simulations, we optimize several material-specific parameters in the model to match the experimental chain length distribution and persistence length. Our approach gives a systematic way to obtain accurate potentials for critical Casimir induced patchy particle interactions and can be used in large-scale simulations. Casimir effect ; Statistical physics ; Monte Carlo method ; Particle (ecology) ; Colloidal particle ; Physics ; Colloid ; Classical mechanics ; Mathematics ; Chemistry Material Dynamics and Properties ; Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems
Authors: Daniel Sanín-Villa | Oscar D. Monsalve-Cifuentes | Elkin Edilberto Henao‐Bravo
Source: Energies (article, 2021-11-30)
Field: Materials Science — Materials Chemistry

Evaluation of Thermoelectric Generators under Mismatching Conditions Due to the wide usability of thermoelectric generators (TEG) in the industry and research fields, it is plausible that mismatching conditions are present on the thermal surfaces of a TEG device, which induces negative-performance effects due to uneven surface temperature distributions. For this reason, the objective of this study is to characterize numerically the open-circuit electric output voltage of a TEG device when a mismatching condition is applied to both the cold and hot sides of the selected N and P-type semiconductor material Bi0.4Sb1.6Te3. A validated numerical simulation paired with a parametric study is conducted using the Thermal-Electric module of ANSYS 2020 R1, for which different thermal boundary and mismatching conditions are applied while considering the temperature-dependent thermoelectrical properties of the N and P-type material. The results show an inverse relationship between the open-circuit voltage and the mismatching temperature difference. When a mismatching condition is applied on the hot side of the TEG device, the temperature-dependent electrical resistance has lower values, deriving in higher voltage results (linear tendency) compared to a mismatching condition applied to the cold side (non-linear tendency). Open-circuit voltage ; Voltage ; Thermoelectric effect ; Materials science ; Parametric statistics ; Boundary value problem ; Thermal ; Thermoelectric generator ; Semiconductor ; Mechanics ; Optoelectronics ; Electrical engineering ; Mathematics ; Thermodynamics ; Physics ; Mathematical analysis ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Heat Transfer and Optimization
Authors: Els Du Bois | Lore Veelaert | Emiel Tormans | Ingrid Moons
Source: Proceedings of the Design Society (article, 2021-07-27)
Field: Materials Science — Materials Chemistry

HOW SHOULD PLASTIC RECYCLATES LOOK LIKE TO BE PERCEIVED AS SUSTAINABLE: A FIRST EXPLORATION Abstract Notwithstanding the positive environmental impact of recycled plastics, they are only scarcely used in new designs due to unfamiliarity and lack of material identities. This research aims to touch upon the sensorial attributes that characterise the sustainable perception of recycled plastics. Understanding this would allow to respond to the new trend of sustainable living by offering recycled materials that are successfully perceived as sustainable, and that could support the identity building of each specific recycled plastic material. Three research activities were executed to explore (i) the designerly understanding of sustainable perceived plastics; (ii) the consumer understanding of sensorial material attributes that influence the sustainable perception; (iii) the understanding of these attributes towards recycled plastic materials. Five variables were found that interfere with the perception of the participants: A weaker colour intensity, the use of colourless colours, a rougher texture, a speckled structure and the usage of a matte gloss can give a sustainable look towards a sustainable plastic material. Further research should detail these variables, its limitations and try to make defined guidelines to avoid greenwashing. Greenwashing ; Gloss (optics) ; Perception ; Plastic waste ; Sustainable products ; Sustainable design ; Business ; Sustainability ; Psychology ; Materials science ; Engineering ; Waste management ; Nanotechnology Material Selection and Properties ; Design Education and Practice ; Color perception and design
Authors: Hui Shao | Wenmin Huang | Luisana Avilán | Véronique Receveur‐Brechot | Carine Puppo | Rémy Puppo | Régine Lebrun | Brigitte Gontero | Hélène Launay
Source: Cell Communication and Signaling (article, 2021-03-24)
Field: Chemistry — Spectroscopy

A new type of flexible CP12 protein in the marine diatom Thalassiosira pseudonana These findings bring new insights into the large family of dynamic proteins containing disordered regions, thus increasing the diversity of known CP12 proteins. As it is a protein that is more abundant in many stresses, it is not devoted to one metabolism and in particular, it is not specific to carbon metabolism. This raises questions about the role of this protein in addition to the well-established regulation of the CBB cycle. Choregraphy of metabolism by CP12 proteins in Viridiplantae and Heterokonta. While the monomeric CP12 in Viridiplantae is involved in carbon assimilation, regulating phosphoribulokinase (PRK) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) through the formation of a ternary complex, in Heterokonta studied so far, the dimeric CP12 is associated with Ferredoxin-NADP reductase (FNR) and GAPDH. The Viridiplantae CP12 can bind metal ions and can be a chaperone, the Heterokonta CP12 is more abundant in all stresses (C, N, Si, P limited conditions) and is not specific to a metabolism. Video Abstract. Thalassiosira pseudonana ; Electrospray ionization ; Diatom ; Circular dichroism ; Chemistry ; Mass spectrometry ; Biology ; Crystallography ; Biophysics ; Biochemistry ; Botany ; Chromatography Mass Spectrometry Techniques and Applications ; Photosynthetic Processes and Mechanisms ; Advanced Proteomics Techniques and Applications
Authors: Jiangang Shen | Weiming Qiao | Huizhe Chen | Jun Zhou | Fei Liu
Source: Applied Sciences (article, 2021-05-31)
Field: Chemistry — Analytical Chemistry

Application of Visible/Near Infrared Spectrometers to Quickly Detect the Nitrogen, Phosphorus, and Potassium Content of Chemical Fertilizers The rapid determination of nitrogen, phosphorus, potassium and other major nutrient elements is an important technical guarantee in the quality control of chemical fertilizers. In this study, a small visible spectrometer and a small near-infrared spectrometer were used to collect spectrum information of 33 different common chemical fertilizers including compound fertilizers, blended fertilizers and controlled-release fertilizers. The 550~950 nm and 1050~1640 nm spectra with stable signals were intercepted as the analysis spectrum, and the competitive adaptive reweighted sampling algorithm (CARS) was used to select 161, 229, and 161 spectral characteristic wavelengths for the three nutrient contents of N, P2O5, and K2O respectively. The partial least squares (PLS) and extreme learning machine (ELM) models of N, P2O5, and K2O were established based on the 550~950 nm waveband, 1050~1640 nm waveband, full spectrum, and characteristic wavelength, respectively. The coefficient of determination (R2), root mean square error (RMSE), and residual predictive deviation (RPD) were used to evaluate the effect of the model. With the optimal prediction models, the values of Rp2 for N, P2O5, and K2O were 0.989, 0.963, 0.981, and for RPD were 9.71, 5.09, 7.29, respectively. The research results show that Vis/NIR spectroscopy can predict the content of nitrogen, phosphorus, and potassium nutrients in fertilizers, and the near-infrared band from 1050 nm to 1640 nm has a better prediction effect. The characteristic wavelength selection reduces the spectral variables by 9/10, and the performance of the model based on the characteristic wavelength is close to that of the full-spectrum model. Potassium ; Partial least squares regression ; Spectrometer ; Nitrogen ; Phosphorus ; Near-infrared spectroscopy ; Wavelength ; Nutrient ; Chemistry ; Analytical Chemistry (journal) ; Environmental science ; Materials science ; Environmental chemistry ; Mathematics ; Optics ; Physics ; Statistics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Bhawana Kumari | Rahul Kumar | Rohit Sharma | Manodipan Sahoo
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Design, Modeling and Analysis of Cu-Carbon Hybrid Interconnects In this work, a new interconnect structure is proposed for the first time where copper-carbon nanotube composite interconnect is encapsulated by graphene barrier layers named here as copper-carbon (Cu-carbon) hybrid interconnects. The motivation behind this new structure is to utilize the enhanced conductivity of copper-carbon nanotube (Cu-CNT) composite and improved reliability of copper-graphene (Cu-GNR) hybrid in order to build a better interconnect structure for possible replacement of copper interconnects in near future VLSI applications. The steps required to fabricate this structure is also proposed by utilizing the fabrication methods of Cu-CNT composite and Cu-GNR hybrid materials. First-principlesbased atomistic simulations suggest that Cu-Carbon hybrid structure is more conductive than its parent structures, i.e. Cu-CNT composite and Cu-GNR hybrid. This deduction is also supported by the circuit simulation results at 7 nm node which show that Cu-Carbon hybrid interconnect experiences least delay among all other alternatives. When compared to Cu-GNR, Cu-CNT and Cu interconnects, delay in 1 mm long Cu-Carbon hybrid interconnect is lesser by 28%, 41% and 88%, respectively. Time-domain analysis suggests that Cu-Carbon hybrid interconnect has the steepest and sharpest step response. Cu-Carbon hybrid interconnect has proven to be superior than other alternatives in terms of signal integrity. Noise-delay-product in a 1 mm long Cu-Carbon hybrid is lesser by 42%, 47% and 84% as compared to Cu-GNR, Cu-CNT and Cu interconnects, respectively. Power consumption is also least in Cu-Carbon hybrid interconnects. Power-delay-product in a 1 mm long Cu-Carbon hybrid is also reduced by 41%, 44% and 43% as compared to Cu-GNR, Cu-CNT and Cu interconnects, respectively. These findings promote Cu-Carbon hybrid interconnect as a superior candidate for near future VLSI applications. Materials science ; Carbon nanotube ; Interconnection ; Graphene ; Composite number ; Copper ; Carbon fibers ; Hybrid material ; Electrical conductor ; Optoelectronics ; Nanotechnology ; Composite material ; Computer science ; Metallurgy ; Telecommunications Graphene research and applications ; Molecular Junctions and Nanostructures ; Semiconductor materials and interfaces
Authors: Furkan M. Torun | Sebastian Virreira Winter | Sophia Doll | Felix M. Riese | Artem Vorobyev | Johannes B. Mueller‐Reif | Philipp E. Geyer | Maximilian T. Strauss
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-03-06)
Field: Chemistry — Spectroscopy

Transparent exploration of machine learning for biomarker discovery from proteomics and omics data Abstract Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ ( https://OmicLearn.com ), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences. Graphical Abstract Highlights OmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discovery The ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledge OmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the dataset Several algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integrated All results, settings and method text can be exported in publication-ready formats Biomarker discovery ; Computer science ; Proteomics ; Python (programming language) ; Upload ; Preprocessor ; Machine learning ; Data science ; Artificial intelligence ; Data mining ; World Wide Web ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Mohammad Hassan Houshdar Tehrani | Mohammadreza Gholibeikian | Abdolhamid Bamoniri | Bi Bi Fatemeh Mirjalili
Source: Frontiers in Endocrinology (review, 2021-01-14)
Field: Chemistry — Organic Chemistry

Cancer Treatment by Caryophyllaceae-Type Cyclopeptides Cancer is one of the leading diseases, which, in the most cases, ends with death and, thus, continues to be a major concern in human beings worldwide. The conventional anticancer agents used in the clinic often face resistance among many cancer diseases. Moreover, heavy financial costs preclude patients from continuing treatment. Bioactive peptides, active in several diverse areas against man's health problems, such as infection, pain, hypertension, and so on, show the potential to be effective in cancer treatment and may offer promise as better candidates for combating cancer. Cyclopeptides, of natural or synthetic origin, have several advantages over other drug molecules with low toxicity and low immunogenicity, and they are easily amenable to several changes in their sequences. Given their many demanded homologues, they have created new hope of discovering better compounds with desired properties in the field of challenging cancer diseases. Caryophyllaceae-type cyclopeptides show several biological activities, including cancer cytotoxicity. These cyclopeptides have been discovered in several plant families but mainly are from the Caryophyllaceae family. In this review, a summary of biological activities found for these cyclopeptides is given; the focus is on the anticancer findings of these peptides. Among these cyclopeptides, information about Dianthins (including Longicalycinin A), isolated from different species of Caryophyllaceae, as well as their synthetic analogues is detailed. Finally, by comparing their structures and cytotoxic activities, finding the common figures of these kinds of cyclopeptides as well as their possible future place in the clinic for cancer treatment is put forward. Caryophyllaceae ; Cancer ; Pharmacogenomics ; Drug discovery ; Computational biology ; Biology ; Medicine ; Bioinformatics ; Genetics ; Ecology Carbohydrate Chemistry and Synthesis ; Chemical Synthesis and Analysis ; Biochemical and Structural Characterization
Authors: Alexandre Mallet | Margaud Pérémé | Lorraine Awhangbo | Cyrille Charnier | Jean‐Michel Roger | Jean‐Philippe Steyer | Éric Latrille | Ryad Bendoula
Source: Waste Management (article, 2021-04-18)
Field: Chemistry — Analytical Chemistry

Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different measurement configurations Spectrometer ; Near-infrared spectroscopy ; Process engineering ; Spectral resolution ; Robustness (evolution) ; Methane ; Environmental science ; Chemistry ; Optics ; Engineering ; Spectral line ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mohammed Raju Ahmed | DeMetris D. Reed | J. M. Young | Sulaymon Eshkabilov | E. P. Berg | Xin Sun
Source: Applied Sciences (article, 2021-05-18)
Field: Chemistry — Analytical Chemistry

Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology Fat content is one of the most important parameters of beef grading. In this study, a hyperspectral imaging (HSI) system, combined with multivariate data analysis, was adopted for the classification of beef grades. Three types of beef samples, namely Akaushi (AK), USDA prime, and USDA choice, were used for HSI image acquisition in the spectral range of 400–1000 nm. Spectral information was extracted from the image by applying the partial least squares discriminant analysis (PLS-DA) for the three classifications. A total of eight different types of data pre-processing procedures were tested during PLS-DA to evaluate their individual performance, with the accepted pre-processing method selected based on the highest accuracy. Chemical and binary images were generated to visualize the fat mapping of the samples. Quantitative analysis of the samples was performed for the reference measurement of the dry matter and fat content. The highest overall accuracy, 86.5%, was found using the Savitzky–Golay second derivatives pre-processing method for PLS-DA analysis. The optimal wavelength values were found from the beta coefficient curve. The chemical and binary images showed significant differences in fat mapping among the three groups of samples, with AK having the greatest intramuscular fat content and USDA choice having the least. Similar results were observed during the proximate analysis. The findings of this study demonstrate that the HSI technique is a potential tool for the fast and non-destructive determination of beef grades based on fat mapping. Hyperspectral imaging ; Intramuscular fat ; Partial least squares regression ; Linear discriminant analysis ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Food science ; Statistics ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: Chih-Chuen Lin | Phani Motamarri | Vikram Gavini
Source: npj Computational Materials (article, 2021-04-12)
Field: Chemistry — Spectroscopy

Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations Abstract We present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L 1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons. Scaling ; Kohn–Sham equations ; Tensor (intrinsic definition) ; Hamiltonian (control theory) ; Mathematics ; Density functional theory ; Mathematical analysis ; Physics ; Quantum mechanics ; Mathematical optimization ; Geometry Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies ; Physics of Superconductivity and Magnetism
Authors: María Gyomar González-González | J. Blasco | Sergio Cubero | Patricia Chueca
Source: Agronomy (article, 2021-05-18)
Field: Chemistry — Analytical Chemistry

Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging Tetranychus urticae Koch is an important citrus pest that produces chlorotic spots on the leaves and scars on the fruit of affected trees. It is detected by visual inspection of the leaves. This work studies the potential of colour and hyperspectral imaging (400–1000 nm) under laboratory conditions as a fast and automatic method to detect the damage caused by this pest. The ability of a traditional vision system to differentiate this pest from others, such as Phyllocnistis citrella, and other leaf problems such as those caused by nutritional deficiencies, has been studied and compared with a more advanced hyperspectral system. To analyse the colour images, discriminant analysis has been used to classify the pixels as belonging to either a damaged or healthy leaves. In contrast, the hyperspectral images have been analysed using PLS DA. The rate of detection of the damage caused by T. urticae with colour images reached 92.5%, while leaves that did not present any damage were all correctly identified. Other problems such as damage by P. citrella were also correctly discriminated from T. urticae. Moreover, hyperspectral imaging allowed damage caused by T. urticae to be discriminated from healthy leaves and to distinguish between recent and mature leaves, which indicates whether it is a recent or an older infestation. Furthermore, good results were achieved in the discrimination between damage caused by T. urticae, P. citrella, and nutritional deficiencies. Tetranychus urticae ; Hyperspectral imaging ; Horticulture ; PEST analysis ; Biology ; Multispectral image ; Infestation ; Botany ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Date Palm Research Studies