jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoqiang Peng | Pengpeng Yang | Kun Dai | Yong Chen | Xiaochun Chen | Wei Zhuang | Hanjie Ying | Jinglan Wu
Source: Scientific Reports (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Synthesis, adsorption and molecular simulation study of methylamine-modified hyper-cross-linked resins for efficient removal of citric acid from aqueous solution A series of methylamine-modified hyper-cross-linked resins were fabricated from chloromethylated polystrene-co-divinylbenzene by two continuous reactions (Friedel-Crafts alkylation and amination). The BET surface area and pore volume of the as-prepared resins took a positive correlation to the reaction time and temperature during alkylation reaction while lessened during amination process. When running batch adsorption experiments for adsorption of citric acid, the methylamine-modified resin named HM-65-2 showed higher adsorption capacity of 136.3 mg/g and selectivity of 6.98 (citric/glucose) than the precursor resins. The pseudo-second-order rate model fitted better than the pseudo-first-order model, implying the adsorption sites distributed on the resins surface tended to be heterogeneous. Subsequently, the interactions between citric acid and the resin were investigated by means of molecular simulation. Simulation result showed the addition of nitrogen-containing groups significantly enhanced the adsorption performance of citric acid. Lastly, the dynamic column experiments were performed to obtain the suitable operating conditions for the citric acid adsorption. Citric acid ; Adsorption ; Methylamine ; Aqueous solution ; Amination ; Chemistry ; Alkylation ; Selectivity ; Reductive amination ; Ammonia ; Inorganic chemistry ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal ; Mesoporous Materials and Catalysis
Authors: Wongi Jang | Jaehan Yun | Younggee Seo | Hongsik Byun | Jian Hou | Jun‐Hyun Kim
Source: Polymers (article, 2020-09-03)
Field: Materials Science — Biomaterials

Mixed Dye Removal Efficiency of Electrospun Polyacrylonitrile–Graphene Oxide Composite Membranes Exfoliated graphene oxide (GO) was reliably modified with a cetyltrimethylammonium chloride (CTAC) surfactant to greatly improve the dispersity of the GO in a polyacrylonitrile (PAN) polymer precursor solution. Subsequent electrospinning of the mixture readily resulted in the formation of GO-PAN composite nanofibers containing up to 30 wt % of GO as a filler without notable defects. The absence of common electrospinning problems associated with clogging and phase separation indicated the systematic and uniform integration of the GO within the PAN nanofibers beyond the typical limits. After thoroughly examining the formation and maximum loading efficiency of the modified GO in the PAN nanofibers, the resulting composite nanofibers were thermally treated to form membrane-type sheets. The wettability and pore properties of the composite membranes were notably improved with respect to the pristine PAN nanofiber membrane, possibly due to the reinforcing filler effect. In addition, the more GO loaded into the PAN nanofiber membranes, the higher the removal ability of the methylene blue (MB) and methyl red (MR) dyes in the aqueous system. The adsorption kinetics of a mixed dye solution were also monitored to understand how these MB and MR dyes interact differently with the composite nanofiber membranes. The simple surface modification of the fillers greatly facilitated the integration efficiency and improved the ability to control the overall physical properties of the nanofiber-based membranes, which highly impacted the removal performance of various dyes from water. Polyacrylonitrile ; Graphene ; Materials science ; Membrane ; Composite number ; Electrospinning ; Oxide ; Chemical engineering ; Composite material ; Polymer chemistry ; Nanotechnology ; Chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Nanomaterials for catalytic reactions
Authors: Emilia Peltola | Anja Aarva | Sami Sainio | Joonas J. Heikkinen | Niklas Wester | Ville Jokinen | Jari Koskinen | Tomi Laurila
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Biofouling affects the redox kinetics of outer and inner sphere probes on carbon surfaces drastically differently – implications to biosensing Biofouling imposes a significant threat for sensing probes used in vivo. Antifouling strategies commonly utilize a protective layer on top of the electrode but this may compromise performance of the electrode. Here, we investigated the effect of surface topography and chemistry on fouling without additional protective layers. We have utilized two different carbon materials; tetrahedral amorphous carbon (ta-C) and SU-8 based pyrolytic carbon (PyC) in their typical smooth thin film structure as well as with a nanopillar topography templated from black silicon. The near edge X-ray absorption fine structure (NEXAFS) spectrum revealed striking differences in chemical functionalities of the surfaces. PyC contained equal amounts of ketone, hydroxyl and ether/epoxide groups, while ta-C contained significant amounts of carbonyl groups. Overall, oxygen functionalities were significantly increased on nanograss surfaces compared to the flat counterparts. Neither bovine serum albumin (BSA) or fetal bovine serum (FBS) fouling caused major effects on electron transfer kinetics of outer sphere redox (OSR) probe Ru(NH<sub>3</sub>)<sub>6</sub><sup>3+</sup> on any of the materials. In contrast, negatively charged OSR probe IrCl<sub>6</sub><sup>2-</sup> kinetics were clearly affected by fouling, possibly due to the electrostatic repulsion between redox species and the anionically-charged proteins adsorbed on the electrode and/or stronger interaction of the proteins and positively charged surface. The OSR probe kinetics were less affected by fouling on PyC, probably due to conformational changes of proteins on the surface. Dopamine (DA) was tested as an inner sphere redox (ISR) probe and as expected, the kinetics were heavily dependent on the material; PyC had very fast electron transfer kinetics, while ta-C had sluggish kinetics. DA electron transfer kinetics were heavily affected on all surfaces by fouling (ΔE<sub>p</sub> increase 30-451%). The effect was stronger on PyC, possibly due to the more strongly adhered protein layer limiting the access of the probe to the inner sphere. Biofouling ; Redox ; Kinetics ; Outer sphere electron transfer ; Inner sphere electron transfer ; Biosensor ; Carbon fibers ; Chemistry ; Bioelectrochemistry ; Chemical engineering ; Nanotechnology ; Materials science ; Inorganic chemistry ; Physical chemistry ; Electrochemistry ; Electrode ; Organic chemistry ; Physics ; Membrane ; Biochemistry ; Ion Polymer Surface Interaction Studies ; Analytical Chemistry and Sensors ; Molecular Junctions and Nanostructures
Authors: Lemonia-Christina Fengou | Iosif Mporas | Evgenia D. Spyrelli | Alexandra Lianou | George‐John E. Nychas
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Estimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors Spectroscopic methods in tandem with machine learning methodologies have attracted considerable research interest for the estimation of food quality. The objective of this study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) coupled with appropriate machine learning regression algorithms for assessing meat microbiological quality. For this purpose, minced pork patties were stored aerobically and under modified atmosphere packaging (MAP) conditions, at isothermal and dynamic temperature conditions. At regular time intervals during storage, samples were subjected to (i) microbiological analysis, (ii) FTIR measurements and (iii) MSI acquisition. The collected FTIR data were processed by feature extraction methods to reduce dimensionality, and subsequently Support Vector Machines (SVM) regression models were trained using spectral features (FTIR and MSI) to estimate microbiological quality of meat (microbial population). The regression models were evaluated with different experimental replicates using distinct meat batches. The performance of the models was evaluated in terms of correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The RMSE values for the microbial population estimation models using FTIR were 1.268 and 1.024 for aerobic and MAP storage, respectively. The performance in terms of RMSE for the MSI-based models was 1.144 for aerobic and 0.923 for MAP storage, while the combination of FTIR and MSI spectra resulted in models with RMSE equal to 1.146 for aerobic and 0.886 for MAP storage. The experimental results demonstrated the potential of estimating the microbiological quality of minced pork meat from spectroscopic data. Mean squared error ; Support vector machine ; Mathematics ; Coefficient of determination ; Population ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Bin Xu | Marco Di Gennaro | Matthieu J. Verstraete
Source: Physical review. B./Physical review. B (article, 2020-10-20)
Field: Materials Science — Materials Chemistry

Thermoelectric properties of elemental metals from first-principles electron-phonon coupling The Seebeck coefficient is one of the key ingredients in thermoelectric properties, and it is often calculated based simply on the electronic band structure, within the frame of Boltzmann's transport theory and the constant relaxation time approximation. Despite the simplicity and popularity of this approximation, its validity is not fully justified even in lightly doped semiconductors, and it breaks down completely in metals. On the other hand, more sophisticated first-principles approaches are available but require the computation of the full electron-phonon coupling. Here, we demonstrate with several simple (alkali and noble) metals viz., Li, Na, K, Cu, Ag, Au, and Pt, that the variational approach based on ab initio couplings can reproduce experimental Seebeck coefficients quantitatively, whereas the constant relaxation time approximation yields significant quantitative discrepancies and often fails to predict the correct sign. Calculations of the electrical resistivity of these metals via the variational approach are also reported. Seebeck coefficient ; Thermoelectric effect ; Boltzmann constant ; Condensed matter physics ; Materials science ; Coupling constant ; Relaxation (psychology) ; Electrical resistivity and conductivity ; Electronic band structure ; Electron ; Phonon ; Coupling (piping) ; Boltzmann equation ; Thermoelectric materials ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Surface and Thin Film Phenomena
Authors: D. M. Mitin | Yury Berdnikov | Alexandr Vorobyev | А М Можаров | S A Raudik | Olga Yu. Koval | Vladimir Neplokh | E. I. Moiseev | Daniil A. Ilatovskii | Albert G. Nasibulin | Ivan S. Mukhin
Source: ACS Applied Materials & Interfaces (article, 2020-11-29)
Field: Materials Science — Materials Chemistry

Optimization of Optoelectronic Properties of Patterned Single-Walled Carbon Nanotube Films We propose a novel strategy to enhance optoelectrical properties of single-walled carbon nanotube (SWCNT) films for transparent electrode applications by film patterning. First, we theoretically considered the effect of the conducting pattern geometry on the film quality factor and then experimentally examined the calculated structures. We extend these results to show that the best characteristics of patterned SWCNT films can be achieved using the combination of initial film properties: low transmittance and high conductivity. The proposed strategy allows the patterned layers of SWCNTs to outperform the widely used indium-tin-oxide electrodes on both flexible and rigid substrates. Materials science ; Carbon nanotube ; Indium tin oxide ; Electrode ; Transmittance ; Conductivity ; Nanotechnology ; Optoelectronics ; Transparent conducting film ; Nanotube ; Thin film ; Composite material Carbon Nanotubes in Composites ; Nanowire Synthesis and Applications ; Neuroscience and Neural Engineering
Authors: Da Zhang | Shuailin Chen
Source: Energies (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Intelligent Recognition of Insulator Contamination Grade Based on the Deep Learning of Ultraviolet Discharge Image Information In order to achieve the noncontact detection of the contamination grade of insulators and to provide guidance for preventing the contamination flashover of insulators based on the pollution state, we propose a contamination grade recognition method based on the deep learning of ultraviolet discharge images using a sparse autoencoder (SAE) and a deep belief network (DBN). Under different humidity conditions, we filmed and preprocessed the ultraviolet discharge images of insulators at different contamination grades and we obtained the ultraviolet spot area sequence as original data for contamination grade recognition. A double-layer sparse autoencoder was used to extract sparse features that could characterize different contamination grades from the ultraviolet spot area sequence. Using the extracted features, a DBN composed of three layers of restricted Boltzmann machine was trained to provide contamination grade recognition. To verify the effectiveness of the method proposed in this paper, high-voltage experiments were performed on contaminated insulators at relative humidity levels of 80%, 85%, and 90%, and ultraviolet images were recorded. The proposed SAE–DBN method was used to identify the ultraviolet images of the insulators with different contamination grades. The recognition accuracy rates at the three humidity levels were 91.25%, 93.125%, and 92.5%. The experimental results showed that this method could accurately recognize the contamination grade of the insulator and provide guidance for the prevention of contamination flashover based on the pollution severity. Contamination ; Ultraviolet ; Artificial intelligence ; Environmental science ; Insulator (electricity) ; Arc flash ; Autoencoder ; Computer science ; Pattern recognition (psychology) ; Deep learning ; Materials science ; Optoelectronics High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Advanced Neural Network Applications
Authors: Mario Miličević | Krunoslav Žubrinić | Ivan Grbavac | Ines Obradović
Source: Remote Sensing (article, 2020-07-02)
Field: Chemistry — Analytical Chemistry

Application of Deep Learning Architectures for Accurate Detection of Olive Tree Flowering Phenophase The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)’s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures. Olea ; Olive trees ; Orchard ; Computer science ; Phenology ; Deep learning ; Artificial intelligence ; Tree (set theory) ; Remote sensing ; Machine learning ; Mathematics ; Ecology ; Horticulture ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Waleed Khan | Nasru Minallah | Imran Ullah Khan | Zahid Wadud | Muhammad Zeeshan | Suhail Yousaf | Abdul Baseer Qazi
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

On the Performance of Temporal Stacking and Vegetation Indices for Detection and Estimation of Tobacco Crop Machine learning in association with remote sensing has assisted agricultural specialists in monitoring, classification and yield estimation of crops. Tobacco is a major taxable crop of Pakistan, however the existing traditional methods for its monitoring and yield estimation are not only expensive and time consuming but also have limitations in terms of accuracy of collected data by a large number of diverse human surveyors. Due to the existence of such loopholes in the employed mechanism for tobacco crop monitoring and yield estimation, its illicit growth and distribution is on the rise. In this paper we have established a sophisticated machine learning mechanism for tobacco crop estimation using temporally stacked sentinel-2 satellite's data of Pakistan. Instead of the conventional approach of using single remotely sensed imagery for the target crop classification, we propose a machine learning based classification algorithm while keeping in view the phonological cycle of the target tobacco crop. Using the proposed mechanism, the temporal variations within the tobacco crop and its association with the variations of other vegetation is considered to improve the classification performance of the employed machine learning algorithm. Furthermore, the impact of stacking the vegetation indices derived from near infrared and vegetation red edge bands of sentinel-2 with the original sentinel-2 datasets, including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Index 45 (NDI45), on the classification performance of the machine learning mechanism is investigated. Ground Truth data for training of our Artificial Neural Networks classifier, was obtained using indigenously developed survey application “GEOSurvey”. Experiments were conducted using our proposed mechanism while considering various input data setups - including single date imagery, temporally stacked datasets based on phonological cycle of tobacco crop and different combinations of NDVI and NDI45 stacking. Our proposed experimental setup consisting of temporally stacked imagery along with NDVI stacking results in the best classification performance of 95.81% with reference to the single date imagery stacked with NDVI and NDI45, with performance gain of 07.32%. Stacking ; Vegetation (pathology) ; Estimation ; Crop ; Vegetation Index ; Computer science ; Remote sensing ; Environmental science ; Normalized Difference Vegetation Index ; Forestry ; Agronomy ; Geography ; Leaf area index ; Medicine ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Saritha Unnikrishnan | John Donovan | Russell Macpherson | David Tormey
Source: IEEE Transactions on Industrial Informatics (article, 2020-01-24)
Field: Chemistry — Analytical Chemistry

An Integrated Histogram-Based Vision and Machine-Learning Classification Model for Industrial Emulsion Processing Existing techniques in emulsion quality evaluation are found to be highly subjective, time-consuming, and prone to overprocessing. Other conventional droplet analysis techniques such as laser diffraction, which require dilution of samples, introduce an additional complexity to industrial processes. The possibility of developing a fully automated technique for droplet characterization during emulsification holds remarkable potential for overcoming the existing challenges. In this article, a histogram-based image segmentation technique detects droplets from emulsion micrographs. The evolution of droplet characteristics and their significance are studied by performing statistical analysis, and the significant characteristics are selected. The principal component analysis is applied to obtain a reduced set of uncorrelated components from the selected characteristics. The linear discriminant analysis classifies the micrographs into a set of quality categories called target, acceptable, marginal, and unacceptable. The model accuracy is validated using stratified five-fold cross-validation and is successful in classifying the micrographs obtained from two different manufacturing facilities with high accuracy up to 100%. The histogram-based technique is successful in detecting smaller droplets than previously reflected in the literature. The current approach is fully automated and is implemented as a soft-sensor, which supports its real-time deployment into an industrial environment. The entire approach has promising potential in the in-line prediction of emulsion quality leading to more efficient and sustainable manufacturing. Histogram ; Artificial intelligence ; Computer science ; Principal component analysis ; Pattern recognition (psychology) ; Automation ; Linear discriminant analysis ; Image processing ; Computer vision ; Machine vision ; Engineering ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Innovative Microfluidic and Catalytic Techniques Innovation