jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenxi Zhai | Yang Yu | Yumei Zhu | Jing Zhang | Ying Zhong | Jingjie Yeo | Mingchao Wang
Source: Molecules (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

The Impact of Foaming Effect on the Physical and Mechanical Properties of Foam Glasses with Molecular-Level Insights Foaming effect strongly impacts the physical and mechanical properties of foam glass materials, but an understanding of its mechanism especially at the molecular level is still limited. In this study, the foaming effects of dextrin, a mixture of dextrin and carbon, and different carbon allotropes are investigated with respect to surface morphology as well as physical and mechanical properties, in which 1 wt.% carbon black is identified as an optimal choice for a well-balanced material property. More importantly, the different foaming effects are elucidated by all-atomistic molecular dynamics simulations with molecular-level insights into the structure-property relationships. The results show that smaller pores and more uniform pore structure benefit the mechanical properties of the foam glass samples. The foam glass samples show excellent chemical and thermal stability with 1 wt.% carbon as the foaming agent. Furthermore, the foaming effects of CaSO<sub>4</sub> and Na<sub>2</sub>HPO<sub>4</sub> are investigated, which both create more uniform pore structures. This work may inspire more systematic approaches to control foaming effect for customized engineering needs by establishing molecular-level structure-property-process relationships, thereby, leading to efficient production of foam glass materials with desired foaming effects. Materials science ; Foaming agent ; Composite material ; Dextrin ; Carbon fibers ; Carbon nanofoam ; Thermal stability ; Work (physics) ; Chemical engineering ; Porosity ; Chemistry ; Mechanical engineering ; Organic chemistry Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Glass properties and applications
Authors: Umar Sa’ad Aliyu | I.G. Geidam | Muhammed Sani Otto | M. Yousuff Hussaini
Source: Journal of the Nigerian Society of Physical Sciences (article, 2022-02-27)
Field: Materials Science — Ceramics and Composites

Investigations of the Elastic Moduli of Er2O3 NPs Doped TeO2 – B2O3 – SiO2 Glasses using Theoretical Models Elastic moduli of {[(TeO2)0.7 (B2O3)0.3]0.8 (SiO2)0.2}1-y (Er2O3 NPs)y glasses with y = 0.01, 0.02, 0.03, 0.04, 0.05 were studied in this work using the theoretical elastic models. The Makishima &amp; Mackenzie, Rocherulle and bond compression models were employed for the study. In the Makishima and Mackenzie model, the packing density was calculated from the bulk glass molar weight and the bulk glass density whereas in Rocherulle model it is determined as the individual oxides. Young, shear and bulk moduli as well as the Poisson ratio were calculated for the glasses in the Makishima and Rocherulle models, while longitudinal, was calculated in addition to young, bulk and shear moduli using the bond compression model. Bond per unit volume number (nb), bulk modulus, bulk modulus ratio (Kbc/Ke), atomic ring size (?) and stretching force constant were also calculated and presented. The values of the Young, bulk and shear moduli obtained from Makishima model increased from 52.854 to 55.335 GPa, 35.754 to 39.862 GPa and 21.080 to 21.809 GPa respectively with Er2O3 NPs composition increase from 1% to 5%.. The Rocherulle model presented increasing values for Young, bulk and shear moduli as 56.910 to 58.432 GPa, 41.452 to 44.450 GPa and 22.385 to 22.809 GPa respectively with Er2O3 NPs composition increase from 1% to 5%. The bond compression model presented much higher values of the elastic moduli compared to the experimentally obtained values and showed an increasing trend as the Er2O3 NPs concentration increases. In the glass network, the atomic ring size value decreased from 0.5698 to 0.5091 nm indicating an increase in the close packing of atoms. Based on the elastic moduli values presented by all the models, Makishima and Mackenzie model presented a more reliable data and hence represents the best model for the studied glass system. Bulk modulus ; Materials science ; Shear modulus ; Elastic modulus ; Moduli ; Composite material ; Shear (geology) ; Modulus ; Poisson's ratio ; Bond length ; Young's modulus ; Mineralogy ; Crystallography ; Poisson distribution ; Chemistry ; Crystal structure ; Mathematics ; Physics Glass properties and applications ; Luminescence Properties of Advanced Materials ; Crystal Structures and Properties
Authors: Aksel S. Obdrup | D. C. Florian Wieland | Mathias K. Huss‐Hansen | Matthias M. L. Arras | Matti Knaapila
Source: Polymer Testing (article, 2022-03-07)
Field: Materials Science — Materials Chemistry

Classifying condition of ultra-high-molecular-weight polyethylene ropes with wide-angle X-ray scattering Ropes of ultra-high-molecular-weight polyethylene (UHMWPE) are replacing steel wires in many applications and nondestructive testing to monitor their condition is of scientific and commercial interest. In this work, wide-angle X-ray scattering (WAXS) combined with linear discriminant analysis (LDA) is proposed as classification method to distinguish between healthy and damaged UHMWPE ropes. Healthy (as produced, after pre-stretching) and damaged (in-field use) ropes (⌀=22mm) have been analyzed using synchrotron radiation. Firstly, it is demonstrated that scans of healthy and damaged ropes can be distinguished with 100% cross-validated test classification accuracy using LDA; this is shown both with the input data consisting of pre-processed 1D WAXS data and with physical parameters retrieved by fitting the WAXS data. Secondly, it is demonstrated that the classification performance is similar using the two forms of input data and that the noise could be increased by a factor of three while maintaining 100% test classification accuracy across all the three cross-validation folds. Materials science ; Ultra-high-molecular-weight polyethylene ; Small-angle X-ray scattering ; Scattering ; Polyethylene ; X-ray ; Composite material ; Optics ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Cultural Heritage Materials Analysis
Authors: Liangqing Zhang | Shu‐Gui Yang | Longgui Peng | Kepeng Zhong | Yanhui Chen
Source: Nanomaterials (article, 2022-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimized Properties in Multifunctional Polyphenylene Sulfide Composites via Graphene Nanosheets/Boron Nitride Nanosheets Dual Segregated Structure under High Pressure The practical application of polymer composites in the electronic and communications industries often requires multi-properties, such as high thermal conductivity (TC), efficient electromagnetic interference (EMI) shielding ability with low electrical conductivity, superior tribological performance, reliable thermal stability and excellent mechanical properties. However, the integration of these mutually exclusive properties is still a challenge, ascribed to their different requirement on the incorporated nanofillers, composite microstructure as well as processing process. Herein, a well-designed boron nitride nanosheet (BN)/graphene nanosheet (GNP)/polyphenylene sulfide (PPS) composite with a dual-segregated structure is fabricated via high-pressure molding. Rather than homogenous mixing of the hybrid fillers, GNP is first coated on PPS particles and followed by encapsulating the conductive GNP layers with insulating BN, forming a BN shell-GNP layer-PPS core composite particles. After hot-pressing, a dual segregated structure is constructed, in which GNP and BN are distinctly separated and arranged in the interfaces of PPS, which on the one hand gives rise to high thermal conductivity, and on the other hand, the aggregated BN layer can act as an "isolation belt" to effectively reduce the electronic transmission. Impressively, high-pressure is loaded and it has a more profound effect on the EMI shielding and thermal conductive properties of PPS composites with a segregated structure than that with homogenous mixed-structure composites. Intriguingly, the synergetic enhancement effect of BN and GNP on both thermal conductive performance and EMI shielding is stimulated by high pressure. Consequently, PPS composites with 30 wt% GNP and 10 wt% BN hot-pressed under 600 MPa present the most superior comprehensive properties with a high TC of 6.4 W/m/K, outstanding EMI SE as high as 70 dB, marvelous tribological performance, reliable thermal stability and satisfactory mechanical properties, which make it promising for application in miniaturized electronic devices in complex environments. Materials science ; Composite material ; Nanosheet ; Boron nitride ; Graphene ; Composite number ; Nanocomposite ; Thermal stability ; Thermal conductivity ; Microstructure ; Nanotechnology ; Chemical engineering Electromagnetic wave absorption materials ; Tribology and Wear Analysis ; Dielectric materials and actuators
Authors: Rituparna Das | Arundhati Pal | Amal Paul
Source: BioTechnologia (article, 2022-09-29)
Field: Materials Science — Biomaterials

Enhanced productionof poly(3-hydroxybutyrate-co-3-hydroxyvalerate) copolymerby endophytic Bacillus cereus RCL 02 utilizing sugarcanemolasses as sole source of carbon:a statistical optimization approach Polymers of biological origin have become a topic of interest due to growing concerns about the environmental impact of the disposal of plastics. In recent years, the production of ecobenign microbial polymer polyhydroxyalkanoates (PHAs) using inexpensive and renewable resources has gained significant interest as these compounds are highly biodegradable, biocompatible, and sustainable. This study used leaf endophytic isolate <i>Bacillus cereus</i> RCL 02, obtained from the oil-yielding plant <i>Ricinus communis</i> L., to achieve statistical optimization of culture variables for the enhanced production of PHAs utilizing sugarcane molasses as the sole carbon source. A three-level and four-factor Box-Behnken design of response surface methodology was implemented to optimize the process variables, namely molasses (carbon substrate), ammonium sulfate (nitrogen source), initial pH, and incubation period, for improved biomass formation and PHA production. The highest growth (14.8 g/l) and PHA production (85.2%, dry cell weight) by the isolate were observed with 47 g/l molasses, 3 g/l ammonium sulfate, an initial pH of 6.7, and 62 h of incubation. Statistical optimization of the process allowed achieving a 1.6-fold increase in the PHA yield (7.8-12.6 g/l) compared with the conventional single-factor system of analysis. The biopolymer thus produced was confirmed as a copolymer of 3-hydroxybutyrate and 3-hydroxyvalerate [P(3HB-co-3HV)] using <sup>1</sup>H nuclear magnetic resonance spectroscopic analysis and was found to contain 7.8 mol% 3-hydroxyvalerate. These findings clearly indicate the efficacy of the <i>B. cereus</i> RCL 02 isolate in the biotransformation of raw sugarcane molasses to P(3HV-co-3HV), without the need for supplementation with high-cost precursors. Polyhydroxyalkanoates ; Box–Behnken design ; Biopolymer ; Bacillus cereus ; Biomass (ecology) ; Chemistry ; Response surface methodology ; Food science ; Ammonium ; Biotechnology ; Pulp and paper industry ; Organic chemistry ; Polymer ; Chromatography ; Biology ; Bacteria ; Agronomy biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Biofuel production and bioconversion
Authors: Tayyaba Samreen | Sehar Rasool | Sehrish Kanwal | Safia Riaz | Sidra-Tul-Muntaha | Muhammad Zulqernain Nazir
Source:  (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

Role of Nanotechnology in Precision Agriculture Nanotechnology is an interdisciplinary study field that attempts to boost agricultural output through substantial nanotechnology. This study has been conducted because of the reckless use of pesticides and synthetic fertilizers brought on by the green revolution, which has diminished soil biodiversity and increased disease and insect resistance. Only nanoparticles or nano chips can produce sophisticated biosensors for precision farming and deliver ingredients to plants in a nanoparticle-mediated manner. The precise distribution of nutrients and agrochemicals to plants is made possible by nano-encapsulated versions of conventional fertilizers, insecticides, and herbicides. Nanotechnology-based tests for detecting plant viral diseases are also gaining popularity and are useful for making a rapid and accurate diagnosis of viral disorders. The advantages and future uses of nanotechnology in precision agriculture are covered in this article. Modern technologies and methods based on nanotechnology can solve many issues in traditional agriculture and could revolutionize this industry. Agrochemical ; Nanotechnology ; Agriculture ; Precision agriculture ; Applications of nanotechnology ; Biotechnology ; Biochemical engineering ; Computer science ; Engineering ; Environmental science ; Materials science ; Biology ; Ecology Nanoparticles: synthesis and applications ; Smart Agriculture and AI ; Biosensors and Analytical Detection
Authors: Juntae Kim | Dennis Semyalo | Tae-Gyun Rho | Hyungjin Bae | Byoung–Kwan Cho
Source: Sensors (article, 2022-12-14)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Abnormal Chicken Eggs by Using an Optimized Spectral Analysis System Environmental pressures, such as temperature and light intensity, food, and genetic factors, can cause chicken eggs to develop abnormalities. The common types of internal egg abnormalities include bloody and damaged egg yolk. Spectrometers have been recently used in real-time abnormal egg detection research. However, there are very few studies on the optimization of measurement systems. This study aimed to establish optimum parameters for detecting of internal egg abnormalities (bloody and damaged-yolk eggs) using visible and near-infrared (Vis/NIR) spectrometry (192-1110 nm range) and multivariate data analysis. The detection performance using various system parameters, such as the types of light sources, the configuration of the light, and sensor positions, was investigated. With the help of collected data, a partial least-squares discriminant analysis (PLS-DA) model was developed to classify normal and abnormal eggs. The highest classification accuracy for the various system parameters was 98.7%. Three band selection methods, such as weighted regression coefficient (WRC), sequential feature selection (SFS), and successive projection algorithm (SPA) were used for further model optimization, to reduce the spectral bands from 1028 to less than 7. In conclusion the results indicate that the types of light sources and the configuration design of the sensor and illumination affect the detection accuracy for abnormal eggs. Partial least squares regression ; Linear discriminant analysis ; Feature selection ; Projection (relational algebra) ; Pattern recognition (psychology) ; Multivariate statistics ; Yolk ; Selection (genetic algorithm) ; Artificial intelligence ; Light intensity ; Biological system ; Spectrometer ; Computer science ; Analytical Chemistry (journal) ; Mathematics ; Materials science ; Statistics ; Chromatography ; Biology ; Chemistry ; Optics ; Algorithm ; Physics ; Ecology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Daniel Geiszler | Daniel A. Polasky | Fengchao Yu | Alexey I. Nesvizhskii
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-13)
Field: Chemistry — Spectroscopy

Mining for ions: diagnostic feature detection in MS/MS spectra of post-translationally modified peptides Abstract Post-translational modifications (PTMs) are an area of great interest in proteomics, with a surge in methods to detect them in recent years. However, PTMs can introduce complexity into proteomics searches by fragmenting in unexpected ways. Detecting post-translational modifications in mass spectrometry-based proteomics traditionally relies on identifying ions shifted by the masses of the modifications. This presents challenges for many PTMs. Labile PTMs lose part of their modification mass during fragmentation, rendering shifted fragment ions unidentifiable, and isobaric PTMs are indistinguishable by mass, requiring other diagnostic ions for disambiguation. Furthermore, even modifications that have undergone extensive characterization often produce different fragmentation patterns across instruments and conditions. To address these deficiencies and facilitate the next generation of PTM identification, we have developed a method to automatically find diagnostic spectral features for any PTM, allowing subsequent searches to take advantage of additional metrics and increase PTM identification and localization rates. The method has been incorporated into the open-search annotation tool PTM-Shepherd and the FragPipe computational platform. Fragmentation (computing) ; Proteomics ; Computer science ; Posttranslational modification ; Computational biology ; Identification (biology) ; Mass spectrometry ; Rendering (computer graphics) ; Chemistry ; Artificial intelligence ; Biology ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Karolina Brkić Bubola | Šimun Kolega | Šime Marcelić | Zoran Šikić | Ana Gašparović Pinto | Marko Zorica | Dora Klisović | Anja Novoselić | Maja Jukić Špika | Tomislav Kos
Source: Foods (article, 2022-06-15)
Field: Chemistry — Organic Chemistry

Effect of Different Watering Regimes on Olive Oil Quality and Composition of Coratina Cultivar Olives Grown on Karst Soil in Croatia Croatian islands are olive growing areas characterized by poor conditions for olive trees because of karst soil and a precipitation deficiency. Under these conditions, irrigation is a very important factor for constant olive oil production. This paper aims to investigate the effects of different watering regimes on quantity, sensory and chemical quality and composition of Coratina cv. olive oil obtained from trees grown on ameliorate karst soil during two harvesting years. Olive trees were subjected to rainfed conditions and three different irrigation treatments (T<sub>1</sub>-deficit irrigation representing the usual producer's practice, T<sub>2</sub>-regulated deficit irrigation in respect to phenological stages, T<sub>3</sub>-full irrigation). Irrigation treatments increased oil yield compared to rainfed conditions (T<sub>1</sub> + 58%, T<sub>2</sub> + 66% and T<sub>3</sub> + 74%, representing average values for both studied years). All olive oil samples were of extra quality. Irrigation led to a decrease in carotenoids, volatiles, polyunsaturated fatty acids and linolenic acid contents, with no difference found among irrigation treatments. Total phenols and secoiridoids concentration was not affected by irrigation, indicating that similar oil quality could be achieved with less demand on the water supply. Obtained results could help producers to define a suitable irrigation management in particular conditions of ameliorate karst. Irrigation ; Olive trees ; Deficit irrigation ; Cultivar ; Agronomy ; Karst ; Environmental science ; Phenology ; Horticulture ; Irrigation management ; Biology Edible Oils Quality and Analysis ; Horticultural and Viticultural Research ; Nuts composition and effects
Authors: Fang Jing | Xiu Jin | Lin Wu | Yuxin Zhang | Bing Jia | Zhenfeng Ye | Wei Heng | Li Liu
Source: Foods (article, 2022-11-14)
Field: Chemistry — Analytical Chemistry

Prediction Models for the Content of Calcium, Boron and Potassium in the Fruit of ‘Huangguan’ Pears Established by Using Near-Infrared Spectroscopy It has been proved that the imbalance of the proportion of elements of 'Huangguan' pears in the pulp and peel, especially calcium, boron and potassium, may be important factors that can seriously affect the pears' appearance quality and economic benefits. The objective of this study was to predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears nondestructively and conveniently by using near-infrared spectroscopy (900-1700 nm) technology. Firstly, 12 algorithms were used to preprocess the original spectral data. Then, based on the original and preprocessed spectral data, full-band prediction models were established by using Partial Least Squares Regression and Gradient Boosting Regression Tree. Finally, the characteristic wavelengths were extracted by Genetic Algorithms to establish the characteristic wavelength prediction models. According to the prediction results, the value of the determination coefficient of the prediction sets of the best prediction models for the three elements all reached ideal levels, and the values of their Relative analysis error also showed high levels. Therefore, the micro near-infrared spectrometer based on machine learning can predict the content of calcium, boron and potassium in the pulp and peel of 'Huangguan' pears accurately and quickly. The results also provide an important scientific theoretical basis for further research on the degradation of the quality of 'Huangguan' pears caused by a lack of nutrients. Boron ; Potassium ; PEAR ; Partial least squares regression ; Pulp (tooth) ; Biological system ; Near-infrared spectroscopy ; Materials science ; Computer science ; Machine learning ; Chemistry ; Optics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Horticultural and Viticultural Research
Authors: Željko Penava | Diana Šimić Penava | Željko Knezić
Source: Materials (article, 2022-02-05)
Field: Materials Science — Polymers and Plastics

Heat as a Conductivity Factor of Electrically Conductive Yarns Woven into Fabric In recent years, more and more researchers have been focused on electrically conductive textiles that generate heat or transmit electrical signals and energy to embedded electrical components. In this paper, the dissipation of heat due to the flow of electric current at given voltages is investigated, and at the same time it is determined how this heat affects the change in the electrical resistance of the electrically conductive yarn in the immediate surroundings. Three fabric samples were woven in a plain weave with three types of different electrically conductive yarns. Three electrically conductive yarns are woven in parallel in the weft direction and separated from each other by one polyester (PES) yarn due to electrical insulaton. Conductive yarns are electrically connected so that the outer yarns are used for heating by the flow of electric current at a certain constant voltage, and the central yarn is used only to measure changes in electrical resistance. When electrothermally conductive fabrics are subjected to certain voltages over time, experimental results have shown that resistance values increase over a short period of time and then gradually decrease, while the temperature gradually increases and stabilizes over time. Based on the analysis of the obtained results of the ratio between the values of applied voltage and temperature to the electrically conductive yarns, the value of thermal dissipation in conductive yarns can be calculated in advance depending on the applied voltage. Furthermore, the obtained results can be further used in applications where conductive yarns are used as heaters for realistic prediction of the obtained heat. Electrical conductor ; Materials science ; Composite material ; Voltage ; Electrically conductive ; Yarn ; Electrical resistivity and conductivity ; Woven fabric ; Polyester ; Electrical resistance and conductance ; Dissipation ; Electrical engineering ; Engineering Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Thermal Radiation and Cooling Technologies