jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Stepan Vladimirovitch Lomov | Iskander S. Akmanov | Qiang Liu | Qi Wu | Sergey G. Abaimov
Source: Polymers (article, 2023-01-29)
Field: Materials Science — Materials Chemistry

Negative Temperature Coefficient of Resistance in Aligned CNT Networks: Influence of the Underlying Phenomena Temperature dependence of electrical conductivity/resistivity of CNT networks (dry or impregnated), which is characterised by a temperature coefficient of resistance (TCR), is experimentally observed to be negative, especially for the case of aligned CNT (A-CNT). The paper investigates the role of three phenomena defining the TCR, temperature dependence of the intrinsic conductivity of CNTs, of the tunnelling resistance of their contacts, and thermal expansion of the network, in the temperature range 300-400 K. A-CNT films, created by rolling down A-CNT forests of different length and described in Lee et al., Appl Phys Lett, 2015, 106: 053110, are investigated as an example. The modelling of the electrical conductivity is performed by the nodal analysis of resistance networks, coupled with the finite-element thermomechanical modelling of network thermal expansion. The calculated TCR for the film is about -0.002 1/K and is close to the experimentally observed values. Comparative analysis of the influence of the TCR defining phenomena is performed on the case of dry and impregnated films. The analysis shows that in both cases, for an A-CNT film at the studied temperature interval, the main factor affecting a network's TCR is the TCR of the CNTs themselves. The TCR of the tunnelling contacts plays the secondary role; influence of the film thermal expansion is marginal. The prevailing impact of the intrinsic conductivity TCR on the TCR of the film is explained by long inter-contact segments of CNTs in an A-CNT network, which define the homogenised film conductivity. Materials science ; Temperature coefficient ; Composite material ; Nanotechnology Thermal properties of materials ; Carbon Nanotubes in Composites ; Graphene research and applications
Authors: Yu Xie | Cai‐Yun Wang | Ningyue Chen | Zhou Cao | Guangcheng Wu | Bangchen Yin | Yuan Li
Source: Angewandte Chemie International Edition (article, 2023-08-31)
Field: Materials Science — Materials Chemistry

Supramolecular Memristor Based on Bistable [2]Catenanes: Toward High‐Density and Non‐Volatile Memory Devices The ever-increasing demand for data storage and neuromorphic computing calls for innovative, high-density solutions, such as resistive random-access memory (RRAM). However, the integration of resistive switching and rectification at the nanoscale remains a formidable challenge. In this study, we introduce a bistable [2]catenane-based supramolecular junction that simultaneously functions as a resistive switch and a diode. All supramolecular junctions are highly stable and reproducible over thousands of resistive switching cycles, because the nano-confinement of two mechanically interlocked rings can stabilize the radical states of pyridinium moieties under ambient conditions. The successful realization of supramolecular junctions in functionality with a thickness of approximately 2 nm presents a promising avenue for the development of molecule-scale based RRAM for a better solution to high density and energy efficiency. Resistive random-access memory ; Bistability ; Neuromorphic engineering ; Rectification ; Supramolecular chemistry ; Nanotechnology ; Materials science ; Catenane ; Realization (probability) ; Resistive touchscreen ; Nanoscopic scale ; Memristor ; Optoelectronics ; Computer science ; Electronic engineering ; Molecule ; Chemistry ; Electrical engineering ; Engineering ; Voltage Luminescence and Fluorescent Materials ; Photoreceptor and optogenetics research ; Advanced Memory and Neural Computing
Authors: Yuji Hatano | Junya Tanigawa | Akimichi Nakazono | T. Sekiguchi | Shinobu Onoda | Takeshi Ohshima | Takayuki Iwasaki | Mutsuko Hatano
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

A wide dynamic range diamond quantum sensor as an electric vehicle battery monitor To demonstrate the application capability of the diamond quantum sensor as an electric vehicle (EV) battery monitor, we (i) investigated the measurable current in a real car noise level and (ii) compared the linearity with conventional sensors. Consequently, (i) we could measure a 20 mA current pulse even under an external magnetic field of 80 µT, which is larger than that of 50 µT around the EV battery module in a real car during driving. The 20 mA pulse measurement corresponds to the EV battery state of charge estimation accuracy of 0.2% in the standard driving pattern, which is smaller than the present level of 10%. (ii) The linearity degradation seen in the Hall sensor near the upper limit of the measurement range was not seen in the diamond sensor. Although the Hall sensor and the shunt resistor showed linearity degradation in the current range of several tens of amperes or less, the degradation was smaller for the diamond sensor. The transverse magnetic field effect in the diamond sensor on the linearity was estimated to be less than 0.01% for a several-degree misalignment of the sensor surface to the magnetic field direction and under a 340 A current. This article is part of the Theo Murphy meeting issue 'Diamond for quantum applications'. Diamond ; Linearity ; Electrical engineering ; Quantum sensor ; Resistor ; Dynamic range ; Magnetic field ; Optoelectronics ; Materials science ; Ampere ; Physics ; Quantum limit ; Voltage ; Quantum ; Optics ; Engineering ; Quantum computer Diamond and Carbon-based Materials Research ; Magnetic Field Sensors Techniques ; Atomic and Subatomic Physics Research
Authors: Michel Rocha Baqueta | Federico Marini | Rodrigo Barros Rocha | Patrícia Valderrama | Juliana Azevedo Lima Pallone
Source: Food Research International (article, 2023-06-30)
Field: Chemistry — Analytical Chemistry

Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer Chemometrics ; Coffea canephora ; Near-infrared spectroscopy ; Spectrometer ; Context (archaeology) ; Computer science ; Physics ; Coffea arabica ; Optics ; Geography ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Coffee research and impacts ; Advanced Chemical Sensor Technologies
Authors: M. Jafarypouria | Biltu Mahato | Sergey G. Abaimov
Source: Polymers (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Separating Curing and Temperature Effects on the Temperature Coefficient of Resistance for a Single-Walled Carbon Nanotube Nanocomposite The temperature coefficient of resistance (<i>TCR</i>) determines the electrical performance of materials in electronics. For a carbon nanotube (CNT) nanocomposite, change of resistivity with temperature depends on changes in CNT intrinsic conductivity, tunnelling thresholds and distances, matrix' coefficient of thermal expansion, and other factors. In our study, we add one more influencing factor-the degree of cure. Complexities of the curing process cause difficulties to predict, or even measure, the curing state of the polymer matrix while uncertainty in the degree of cure influences <i>TCR</i> measurements leading to biased values. Here we study the influence of the cure state on the <i>TCR</i> of a single-walled CNT/epoxy polymer nanocomposite. For the given degree of cure, <i>TCR</i> measurements are conducted in the temperature range 25-100 °C, followed by the next 24 h of post-curing and a new cycle of measurements, 8 cycles in total. We find that contrary to industry practice to expect a high degree of cure after 3 h at 130 °C, the curing process is far from reaching the steady state of the material and continues at least for the next 72 h at 120 °C, as we observe by changes in the material electrical resistivity. If <i>TCR</i> measurements are conducted in this period, we find them significantly influenced by the post-curing process continuing in parallel, leading in particular to non-monotonic temperature dependence and the appearance of negative values. The unbiased <i>TCR</i> values we observe only when the material reaches the steady state are no longer influenced by the heat input. The dependence becomes steady, monotonically increasing from near zero value at room temperature to 0.001 1/°C at 100 °C. Carbon nanotube ; Nanocomposite ; Materials science ; Temperature coefficient ; Curing (chemistry) ; Composite material Carbon Nanotubes in Composites ; Transition Metal Oxide Nanomaterials ; Advanced Sensor and Energy Harvesting Materials
Authors: Tianlin Feng | Huizhi Chen | Min Zhang
Source: Foods (article, 2023-09-19)
Field: Chemistry — Analytical Chemistry

Applicability and Freshness Control of pH-Sensitive Intelligent Label in Cool Chain Transportation of Vegetables Freshness is one of the main factors affecting consumers' purchase of food. The freshness indicator labels of packaged fresh green bell pepper (<i>Capsicum annuum</i> L.) and greengrocery (<i>Brassica chinensis</i> L.) were constructed, and pH-sensitive indicator labels based on the dye of anthocyanin and the mixing dye of methyl red and bromothymol blue were prepared in this study. At the same time, the color, chlorophyll content and vitamin C content of vegetables were measured in order to explore the applicability of indicator labels in the cool chain transportation of vegetables. Compared with the nature dye, the chemical dye-type indicator labels are more sensitive to pH changes. The results showed that the mixed indicator intelligent label had the best indication effect, and the MB 2 (mixing 1 g/L methyl red and bromothymol blue solutions at a ratio of 3:2 with a concentration of 70 mL/L in indicator film solution) indicator label could effectively indicate the freshness changes in vegetables during storage. Meanwhile, the color changes of the MB 2-type indicator label were correlated with the colors change of the sample, changes in nutrients, and changes in CO<sub>2</sub> content inside the packaging. In addition, freshness detection models for green bell pepper and greengrocery by using color information of MB 2 intelligent labels were established. Hence, this pH-sensitive label can be applied as a promising intelligent packaging for non-destructively monitoring the freshness of respiratory and non-respiratory climacteric vegetables. Food science ; Chain (unit) ; Control (management) ; Chemistry ; Business ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Quality and Safety Studies
Authors: Paula C. J. Reis | Sara Correa‐García | Julien Tremblay | Aurélie Beaulieu-Laliberté | Douglas G. Muench | Jason M. E. Ahad | Étienne Yergeau | Jérôme Comte | Christine Martineau
Source: FEMS Microbiology Ecology (article, 2023-11-13)
Field: Chemistry — Analytical Chemistry

Microbial degradation of naphthenic acids using constructed wetland treatment systems: metabolic and genomic insights for improved bioremediation of process-affected water Naphthenic acids (NAs) are a complex mixture of organic compounds released during bitumen extraction from mined oil sands that are important contaminants of oil sands process-affected water (OSPW). NAs can be toxic to aquatic organisms and, therefore, are a main target compound for OSPW. The ability of microorganisms to degrade NAs can be exploited for bioremediation of OSPW using constructed wetland treatment systems (CWTS), which represent a possible low energy and low-cost option for scalable in situ NA removal. Recent advances in genomics and analytical chemistry have provided insights into a better understanding of the metabolic pathways and genes involved in NA degradation. Here, we discuss the ecology of microbial NA degradation with a focus on CWTS and summarize the current knowledge related to the metabolic pathways and genes used by microorganisms to degrade NAs. Evidence to date suggests that NAs are mostly degraded aerobically through ring cleavage via the beta-oxidation pathway, which can be combined with other steps such as aromatization, alpha-oxidation, omega-oxidation, or activation as coenzyme A (CoA) thioesters. Anaerobic NA degradation has also been reported via the production of benzoyl-CoA as an intermediate and/or through the involvement of methanogens or nitrate, sulfate, and iron reducers. Furthermore, we discuss how genomic, statistical, and modeling tools can assist in the development of improved bioremediation practices. Bioremediation ; Environmental chemistry ; Degradation (telecommunications) ; Microbial biodegradation ; Chemistry ; Biodegradation ; Metabolic pathway ; Microorganism ; Biochemical engineering ; Biochemistry ; Biology ; Contamination ; Ecology ; Organic chemistry ; Bacteria ; Metabolism ; Computer science Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Benjamin C. Orsburn
Source: Journal of Proteome Research (article, 2023-01-26)
Field: Chemistry — Spectroscopy

Time-of-Flight Fragmentation Spectra Generated by the Proteomic Analysis of Single Human Cells Do Not Exhibit Atypical Fragmentation Patterns Recent work detailed the unique characteristics of fragmentation spectra derived from peptides from single human cells. This valuable report utilized an ultrahigh-field Orbitrap and directly compared the spectra obtained from high-concentration bulk cell HeLa lysates to those obtained from nanogram dilutions of the same and from nanowell-processed single HeLa cells. The analysis demonstrated marked differences between the fragmentation spectra generated at high and single-cell loads, most strikingly, the loss of high-mass y-series fragment ions. As significant differences exist in the physics of Orbitrap and time-of-flight mass analyzers, a comparison appeared warranted. A similar analysis was performed using isolated single pancreatic cancer cells compared to pools consisting of 100 cells. While a reanalysis of the prior Orbitrap data supports the author's original findings, the same trends are not observed in time-of-flight mass spectra of peptides from single human cells. The results are particularly striking when directly comparing the matched intensity fragment values between bulk and single-cell data generated on the same mass analyzers. Instrument acquisition files, processed data, and spectrum libraries are publicly available on MASSIVE via accession MSV000090635. Orbitrap ; Fragmentation (computing) ; HeLa ; Mass spectrum ; Mass spectrometry ; Spectral line ; Chemistry ; Single-cell analysis ; Analytical Chemistry (journal) ; Biology ; Chromatography ; Cell ; Physics ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Biosensors and Analytical Detection
Authors: Wei Yu | Zhaofu Zhang | Xuhao Wan | Hailing Guo | Qingzhong Gui | Peng Yuan | Yifei Li | Wenjie Fu | Dingyi Lu | Yuchen Ye | Yuzheng Guo
Source: Journal of Chemical Theory and Computation (article, 2023-09-14)
Field: Materials Science — Materials Chemistry

Active Learning the High-Dimensional Transferable Hubbard <i>U</i> and <i>V</i> Parameters in the DFT + <i>U</i> + <i>V</i> Scheme Density functional theory (DFT) is a powerful quantum mechanical computational tool to perform electronic structure calculations for materials. Few DFT methods can ensure accuracy and efficiency simultaneously. DFT + <i>U</i> + <i>V</i> is an alternative effective approach to overcome this drawback. However, the accuracy sensitively depends on the self-consistent estimation of the high-dimensional onsite and intersite Hubbard interaction <i>U</i> and <i>V</i> terms. We propose Bayesian optimization using a dropout (BOD) algorithm, one type of active learning method, to optimize <i>U</i> and <i>V</i> terms. The DFT + <i>U</i> + <i>V</i> with <i>U</i>/<i>V</i> obtained by BOD can produce improved electronic properties for diverse bulk materials of comparable quality to the hybrid functionals with lower computational cost compared to the linear response approach. Note that the band gaps calculated by BOD are somewhat different from that of hybrid functionals by simply applying the same <i>U</i>/<i>V</i> parameters as in the case of surface slabs and interfaces, which suggests that the transferability of <i>U</i>/<i>V</i> from the bulk models to slabs and interfaces is not as well as expected. BOD is extended to calculate the <i>U</i>/<i>V</i> parameters for slabs and interfaces and reach similar results as bulk solids. Moreover, we find that the <i>U</i>/<i>V</i> are reasonably transferable between surface slabs and interfaces with different thicknesses under various effects of quantum confinement, which contributes to fast access to the electronic properties of large-scale systems with higher accuracy. Density functional theory ; Transferability ; Hubbard model ; Scheme (mathematics) ; Computer science ; Electronic structure ; Bayesian optimization ; Quantum ; Hybrid functional ; Statistical physics ; Materials science ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Machine learning ; Mathematical analysis Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Electronic and Structural Properties of Oxides
Authors: Yanyan Liang | Matous Mrovec | Yury Lysogorskiy | Miquel Vega‐Paredes | Christina Scheu | Ralf Drautz
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Atomic cluster expansion for Pt–Rh catalysts: From ab initio to the simulation of nanoclusters in few steps Abstract Insight into structural and thermodynamic properties of nanoparticles is crucial for designing optimal catalysts with enhanced activity and stability. In this work, we present a semi-automated workflow for parameterizing the atomic cluster expansion (ACE) from ab initio data. The main steps of the workflow are the generation of training data from accurate electronic structure calculations, an efficient fitting procedure supported by active learning and uncertainty indication, and a thorough validation. We apply the workflow to the simulation of binary Pt–Rh nanoparticles that are important for catalytic applications. We demonstrate that the Pt–Rh ACE is able to reproduce accurately a broad range of fundamental properties of the elemental metals as well as their compounds while retaining an outstanding computational efficiency. This enables a direct comparison of atomistic simulations to high-resolution experiments. Graphical abstract Nanoclusters ; Ab initio ; Materials science ; Workflow ; Cluster (spacecraft) ; Cluster expansion ; Catalysis ; Nanoparticle ; Work (physics) ; Range (aeronautics) ; Computational science ; Chemical physics ; Nanotechnology ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Database Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Takuya Ehiro
Source: Molecular Informatics (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

Feature importance‐based interpretation of UMAP‐visualized polymer space Dimensionality reduction (DR) techniques are used for various purposes such as exploratory data analysis. A commonly employed linear DR technique is principal component analysis (PCA), which is one of the most popular methods for DR. Owing to its linear nature, PCA enables the determination of axes in a low-dimensional space and the calculation of corresponding loading vectors. However, PCA cannot necessarily extract important features of non-linearly distributed data. This study presents a technique aimed at aiding the interpretation of data reduced through non-linear DR methods. In the proposed method, non-linear dimensionally reduced data was clustered via a density-based clustering method. Thereafter, the obtained cluster labels were classified by random forest (RF) classifiers. Further, feature importance (FI) of RF classifiers and Spearman's rank correlation coefficients between predictive probabilities to obtained clusters and original feature values were utilized for characterizing the visualized dimensionally reduced data. The results revealed that the proposed method can provide the interpretable FI-based images of the handwritten digits dataset. Moreover, the proposed method was also applied to the polymer dataset. The study found that incorporating signed FI was advantageous in achieving a meaningful interpretation. Furthermore, Gaussian process regression was utilized to produce intuitive FI-based heatmaps on a 2-dimensional space for greater ease of understanding. Additionally, to enhance the interpretability of the obtained clusters, a feature selection technique called Boruta was applied. The Boruta feature selection method worked effectively to interpret the obtained clusters with limited and commonly important features. Additionally, the study suggested that computing FI solely from substructure-based descriptors could further enhance the interpretability of the results. Finally, the automation of the proposed method was investigated, and through maximizing the target score based on the quality of both the DR and clustering, indicative results were automatically obtained for both the handwritten digits and polymer datasets. Pattern recognition (psychology) ; Feature selection ; Artificial intelligence ; Principal component analysis ; Dimensionality reduction ; Interpretability ; Cluster analysis ; Computer science ; Feature (linguistics) ; Feature vector ; Random forest ; Data mining ; Curse of dimensionality ; Rank (graph theory) ; Overfitting ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: Tuğba Acar | Kübra Nur Çimen | Elif Özalp | Öznur Ilıca | Esma Ahlatcıoğlu Özerol
Source: ChemistrySelect (article, 2023-08-07)
Field: Materials Science — Polymers and Plastics

Recent Advances in Biosensors Based on Conducting Polymers for Biomedical Applications Abstract Biosensors are very crucial applications in the medical field because of their stability, selectivity, sensitivity, reproducibility and linearity. Conducting polymers (CPs), which stand out with their electrical conductivity and easily synthesize and process, are used to improve the properties of biosensors. CPs are organic materials preferred in biosensor use because of both electrical and optical properties, biocompatible, thermal and environmental stability. In this review, a detailed analysis of the literature on biosensors based on conductive polymers and their biomedical applications has been made. It has been presented that conductive polymer‐based biosensors have an important role in biomedical applications. Biosensor ; Nanotechnology ; Materials science ; Conductive polymer ; Polymer ; Biocompatible material ; Biomedical engineering ; Engineering ; Composite material Conducting polymers and applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors