jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ibrahem M. A. Hasan | Hassan M. A. Salman | Olfat M. Hafez
Source: Environmental Science and Pollution Research (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

Ficus-mediated green synthesis of manganese oxide nanoparticles for adsorptive removal of malachite green from surface water The extract of ficus leaves was used to prepare manganese (IV) oxide nanoparticles (MnO<sub>2</sub> NPs) for the first time. Several different analytical techniques were used to characterize the prepared MnO<sub>2</sub> NPs. MnO<sub>2</sub> has spherical crystals that are ~ 7 nm on average in size and have 149.68 m<sup>2</sup>/g of surface area and 0.91 cm<sup>3</sup>/g of total pore volume. Malachite green (MG) dye was then taken out of the water by adsorption using MnO<sub>2</sub> NPs. Optimization of various adsorption parameters resulted in 188.68-277.78 mg/g maximum adsorption capacities at 298-328 K tested temperatures and 99.6% removal of 50 mg/L MG within 90 min using MnO<sub>2</sub> dose of 0.01 g at pH 10 and 298 K. The results were tested using pseudo-first order, pseudo-second order, intraparticle diffusion, Elovich, and Liquid film kinetic models as well as Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models. The most likely models to describe the adsorption process at 298 K are pseudo-second-order kinetics (R<sup>2</sup> = 0.997) with a rate constant of 4 × 10<sup>-4</sup> g/(mg.min) and Langmuir isotherm (R<sup>2</sup> = 0.973). Additionally, the positive values of enthalpy change (3.91-67.81 kJ/mol) and the negative values of Gibb's free energy (- 3.38 to - 19.7 kJ/mol) indicate that the process is endothermic, spontaneous, and thermodynamically feasible. MnO<sub>2</sub> NPs sustained their adsorption efficiency at 90.4% after 5 sorption cycles. MnO<sub>2</sub> appears to be more selective for MG in studies examining the adsorption of various cationic dyes. Lately, the biosynthesized MnO<sub>2</sub> NPs can be utilized to remove MG from aqueous solutions effectively. Malachite green ; Manganese ; Nanoparticle ; Water treatment ; Ecotoxicology ; Chemistry ; Adsorption ; Manganese oxide ; Environmental chemistry ; Nuclear chemistry ; Materials science ; Environmental engineering ; Nanotechnology ; Environmental science ; Organic chemistry Nanoparticles: synthesis and applications ; Water Quality Monitoring Technologies ; Nanomaterials for catalytic reactions
Authors: Benchen Huang | Marco Govoni | Giulia Galli
Source: PRX Quantum (article, 2022-03-10)
Field: Materials Science — Materials Chemistry

Simulating the Electronic Structure of Spin Defects on Quantum Computers We present calculations of both the ground-and excited-state energies of spin defects in solids carried out on a quantum computer, using a hybrid classical-quantum protocol. We focus on the negatively charged nitrogen-vacancy center in diamond and on the double vacancy in 4H SiC, which are of interest for the realization of quantum technologies. We employ a recently developed first-principles quantum embedding theory to describe point defects embedded in a periodic crystal and to derive an effective Hamiltonian, which is then transformed to a qubit Hamiltonian by means of a parity transformation. We use the variational quantum eigensolver (VQE) and quantum subspace expansion methods to obtain the ground and excited states of spin qubits, respectively, and we propose a promising strategy for noise mitigation. We show that by combining zero-noise extrapolation techniques and constraints on electron occupation to overcome the unphysical-state problem of the VQE algorithm, one can obtain reasonably accurate results on near-term-noisy architectures for ground-and excited-state properties of spin defects. Qubit ; Excited state ; Quantum computer ; Hamiltonian (control theory) ; Quantum mechanics ; Physics ; Ground state ; Quantum algorithm ; Quantum error correction ; Quantum ; Mathematics Diamond and Carbon-based Materials Research ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: S Raghavendra | Souvik Ganguli | P. Thirumarai Selvan | Mitali Madhusmita Nayak | Sushovan Chaudhury | Randell U. Espina | Isaac Ofori
Source: Journal of Food Quality (article, 2022-05-23)
Field: Chemistry — Analytical Chemistry

Deep Learning Based Dual Channel Banana Grading System Using Convolution Neural Network Deep learning has recently been hailed as the most advanced computer vision technology for image classification. The invention of convolutional neural network (CNN) simplified the effort of feature engineering. Classification of various stages of fruit maturity using machine learning algorithms is a difficult task since it is difficult to distinguish the visual features of the fruits at different maturity stages. Fruit ripeness is critical in agriculture since it impacts the quality of the fruit. Manually determining the maturity of the fruit has various flaws, including the fact that it takes a long time, needs a lot of labor, and can lead to inconsistencies. In developing countries, agriculture is one of the most important economic sectors. Created system can be employed in the food processing business, in real-life applications where the intelligent system’s accuracy, cost, and speed will improve the production rate and allow satisfying consumer demand. With small number of image samples, the system is capable of automating assembly line related work for classifying bananas along with sufficient overall accuracy. The noninvasive method will also be used to classify other clustered fruits or horticultural crops in the future. The system can either replace or aid human operators who can focus their efforts on fruit selection. The combined merits of RGB and HSI (hyperspectral imaging) for classification of bananas were highlighted in the present study; they have possible application as a model for classification of several types of horticultural produce. The multi-input model’s quick processing time can be a useful and handy technique in the farm field during postharvest procedures. Via a combination of CNN and MLP applied to data collected using RGB and hyperspectral imaging, the multi-input model reliably recognizes bananas with an accuracy level of 98.4 percent as well as an F1-score of 0.97. The AI algorithm predicted the size (large, medium, and microscopic) and perspective (front or rear half) of banana classes with 99 percent accuracy. In comparison to previous studies that simply employed RGB imaging, the presented model revealed the value of integrating RGB imaging and HSI approaches. Computer science ; Convolutional neural network ; Artificial intelligence ; Ripeness ; Deep learning ; Feature engineering ; Machine learning ; Image processing ; Artificial neural network ; RGB color model ; Hyperspectral imaging ; Pattern recognition (psychology) ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Melita C. Low | Nele Schmitz | Laura E. Boeschoten | José Antonio Cabezas | Mathias Cramm | Volker Haag | Gerald Koch | Barbara Rocha Venâncio Meyer-Sand | Kathelyn Paredes-Villanueva | Erin Price | Andrew H. Thornhill | Jo Van Brusselen | Pieter A. Zuidema | Victor Deklerck | Eleanor E. Dormontt | Alison Shapcott | Andrew J. Lowe
Source: IAWA Journal - KU Leuven/IAWA Journal (article, 2022-07-29)
Field: Chemistry — Organic Chemistry

Tracing the world’s timber: the status of scientific verification technologies for species and origin identification Summary Illegal logging and illegal timber trade is a global problem. Anatomical, genetic, and chemical techniques support illegal logging legislation by verifying the species and geographic origin of timber. In principle, these methods can be used to identify timber species and the origin of harvest, however, the availability of specific tests for important timber species is unclear. We review the status of these methods for the top 322 global priority timber taxa. Our results show that for species identification, reference data exist for 100% of taxa using wood anatomy, 86% using genetics, 41% for using DART TOFMS, and 6% using NIRS. For origin identification, data exist for 24% of taxa, with most studies applying genetic approaches (23%). No studies have developed forensic-ready tests for the global priority timber taxa. The review highlights that the current potential for identifying species is greater than for geographic origin and more research focused on determining the geographical origin of timber is required. Based on the current rate, it will take approx. 27 years to generate geographic data for all 322 priority taxa. Finally, we identify research opportunities to improve global timber tracing efforts. Our findings indicate more research is needed, and quickly so that scientific verification can support regulators to combat illegal logging. Identification (biology) ; Taxon ; Logging ; Tracing ; Illegal logging ; Geography ; Legislation ; Biology ; Evolutionary biology ; Ecology ; Computer science ; Political science Wood and Agarwood Research ; Identification and Quantification in Food ; Cultural Heritage Materials Analysis
Authors: Marta Mendes | Gunnar Dittmar
Source: PROTEOMICS (review, 2022-07-11)
Field: Chemistry — Spectroscopy

Targeted proteomics on its way to discovery For a long time, targeted and discovery proteomics covered different corners of the detection spectrum, with targeted proteomics focused on small target sets. This changed with the recent advances in highly multiplexed analysis. While discovery proteomics still pushes higher numbers of identified and quantified proteins, the advances in targeted proteomics rose to cover large parts of less complex proteomes or proteomes with low protein detection counts due to dynamic range restrictions, like the blood proteome. These new developments will impact, especially on the field of biomarker discovery and the possibility of using targeted proteomics for diagnostic purposes. Proteomics ; Computational biology ; Biology ; Data science ; Computer science ; Genetics Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Glycosylation and Glycoproteins Research
Authors: Sajjad Abdollahramezani | Hossein Taghinejad | Tianren Fan | Mahmood Reza Marzban | Ali A. Eftekhar | Ali Adibi
Source: Nanophotonics (article, 2022-08-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconfigurable multifunctional metasurfaces employing hybrid phase‐change plasmonic architecture We present a hybrid device platform for creating an electrically reconfigurable metasurface formed by the integration of plasmonic nanostructures with phase-change material germanium antimony telluride (GST). By changing the phase of GST from amorphous to crystalline through Joule heating, a large range of responses from the metasurface can be achieved. Furthermore, by using the intermediate phases of GST, the metasurface can interact with the incident light in both over-coupling and under-coupling regimes, leading to an inherently broadband response. Through a detailed investigation of the nature of the fundamental modes, we demonstrate that changing the crystalline phase of the GST at the pixel-level enables an effective control over the key properties (i.e., amplitude, phase, and polarization) of incident light. This leads to the realization of a broadband electrically tunable multifunctional metadevice enabling beam switching, focusing, steering, and polarization conversion. Such a hybrid structure offers a high-speed, broadband, and nonvolatile reconfigurable paradigm for electrically programmable optical devices such as switches, holograms, and polarimeters. Plasmon ; Materials science ; Optoelectronics ; Polarization (electrochemistry) ; Broadband ; Photonics ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Liquid Crystal Research Advancements
Authors: Anita Dudek | Maciej Spiegel | Paulina Strugała | Janina Gabrielska
Source: International Journal of Molecular Sciences (article, 2022-05-12)
Field: Chemistry — Organic Chemistry

Analytical and Theoretical Studies of Antioxidant Properties of Chosen Anthocyanins; A Structure-Dependent Relationships The relationship between the structure and the antiradical and antioxidant activities of three anthocyanidins, namely peonidin, petunidin, and delphinidin, and their glucosides was investigated in this study. The ability of anthocyanins to scavenge free radicals was determined using DPPH● assay, whereas the inhibition of peroxidation in liposomes in relation to a model membrane that imitated the composition of a lipid membrane in tumor cells was specified using the fluorimetric method. To explore this issue at the atomistic level, density functional theory studies were applied. It was shown that glycosides performed better than anthocyanidins in protecting membranes against oxidation. The highest redox potential was demonstrated by anthocyanidins with the highest number of hydroxyl groups in the B ring in the order as follows: (Dp > Pt > Pn), and the same relationship was proven for their glucosides. The majority of the compounds studied here proved to be better antioxidants than ascorbic acid. They showed consistent electrodonating properties and though the f-HAT mechanism became more feasible with each consecutive deprotonation. Glycosylation did not have a direct impact on reactivity, apart from peonidin and petunidin in the study of which it was found that this process was responsible for lifting off steric hindrance between B and C rings and rendering certain pathways more feasible. Kinetic and molecular dynamics are essential to properly describe the membrane’s lipid oxidation. Anthocyanidins ; Petunidin ; Peonidin ; Chemistry ; Malvidin ; Anthocyanidin ; DPPH ; Delphinidin ; Antioxidant ; Organic chemistry ; Stereochemistry ; Flavonoid ; Cyanidin ; Pigment Free Radicals and Antioxidants ; Antioxidant Activity and Oxidative Stress ; Photochemistry and Electron Transfer Studies
Authors: Zhixia Xu | Jinye Tong | Tie Jun Cui | Jie Chang | Daniel F. Sievenpiper
Source: Advanced Photonics (article, 2022-07-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Near-field chiral excitation of universal spin-momentum locking transport of edge waves in microwave metamaterials Controlling energy flow in waveguides has attractive potential in integrated devices from radio frequencies to optical bands. Due to the spin-orbit coupling, the mirror symmetry will be broken, and the handedness of the near-field source will determine the direction of energy transport. Compared with well-established theories about spin-momentum locking, experimental visualization of unidirectional coupling is usually challenging due to the lack of generic chiral sources and the strict environmental requirement. In this work, we design a broadband near-field chiral source in the microwave band and discuss experimental details to visualize spin-momentum locking in three different metamaterial waveguides, including spoof surface plasmon polaritons, line waves, and valley topological insulators. The similarity of these edge waves relies on the abrupt sign change of intrinsic characteristics of two media across the interface. In addition to the development of experimental technology, the advantages and research status of interface waveguides are summarized, and perspectives on future research are presented to explore an avenue for designing controllable spin-sorting devices in the microwave band. Metamaterial ; Physics ; Microwave ; Waveguide ; Excitation ; Optics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Topological Materials and Phenomena
Authors: Paul S. Bond | Kevin Cowtan
Source: Acta Crystallographica Section D Structural Biology (article, 2022-08-25)
Field: Materials Science — Materials Chemistry

<i>ModelCraft</i>: an advanced automated model-building pipeline using <i>Buccaneer</i> Interactive model building can be a difficult and time-consuming step in the structure-solution process. Automated model-building programs such as Buccaneer often make it quicker and easier by completing most of the model in advance. However, they may fail to do so with low-resolution data or a poor initial model or map. The Buccaneer pipeline is a relatively simple program that iterates Buccaneer with REFMAC to refine the model and update the map. A new pipeline called ModelCraft has been developed that expands on this to include shift-field refinement, machine-learned pruning of incorrect residues, classical density modification, addition of water and dummy atoms, building of nucleic acids and final rebuilding of side chains. Testing was performed on 1180 structures solved by experimental phasing, 1338 structures solved by molecular replacement using homologues and 2030 structures solved by molecular replacement using predicted AlphaFold models. Compared with the previous Buccaneer pipeline, ModelCraft increased the mean completeness of the protein models in the experimental phasing cases from 91% to 95%, the molecular-replacement cases from 50% to 78% and the AlphaFold cases from 82% to 91%. Pipeline (software) ; Iterated function ; Model building ; Computer science ; Phaser ; Process (computing) ; Completeness (order theory) ; Algorithm ; Simple (philosophy) ; Computational science ; Mathematics ; Programming language ; Engineering ; Physics Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Shinji Sakane | Tomohiro Takaki | Takayuki Aoki
Source: Materials Theory (article, 2022-01-06)
Field: Materials Science — Materials Chemistry

Parallel-GPU-accelerated adaptive mesh refinement for three-dimensional phase-field simulation of dendritic growth during solidification of binary alloy Abstract In the phase-field simulation of dendrite growth during the solidification of an alloy, the computational cost becomes extremely high when the diffusion length is significantly larger than the curvature radius of a dendrite tip. In such cases, the adaptive mesh refinement (AMR) method is effective for improving the computational performance. In this study, we perform a three-dimensional dendrite growth phase-field simulation in which AMR is implemented via parallel computing using multiple graphics processing units (GPUs), which provide high parallel computation performance. In the parallel GPU computation, we apply dynamic load balancing to parallel computing to equalize the computational cost per GPU. The accuracy of an AMR refinement condition is confirmed through the single-GPU computations of columnar dendrite growth during the directional solidification of a binary alloy. Next, we evaluate the efficiency of dynamic load balancing by performing multiple-GPU parallel computations for three different directional solidification simulations using a moving frame algorithm. Finally, weak scaling tests are performed to confirm the parallel efficiency of the developed code. Computer science ; Computation ; Adaptive mesh refinement ; Parallel computing ; Computational science ; Dendrite (mathematics) ; Directional solidification ; CUDA ; Algorithm ; Materials science ; Alloy ; Geometry ; Mathematics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Fabio Di Giuseppe | Fanny Coffigniez | Chahinez Aouf | Valérie Guillard | Elena Torrieri
Source: Food Packaging and Shelf Life (article, 2022-01-20)
Field: Materials Science — Biomaterials

Activated gallic acid as radical and oxygen scavenger in biodegradable packaging film Gallic acid ; Scavenger ; Active packaging ; Chemistry ; Oxygen ; Food packaging ; Free radical scavenger ; Radical ; Chemical engineering ; Antioxidant ; Food science ; Nuclear chemistry ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Paul Boone | Yiwen He | Austin Lieber | Janice A. Steckel | Nathaniel L. Rosi | Katherine Hornbostel | Christopher E. Wilmer
Source: Nanoscale (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Designing optimal core–shell MOFs for direct air capture Metal-organic frameworks (MOFs), along with other novel adsorbents, are frequently proposed as candidate materials to selectively adsorb CO<sub>2</sub> for carbon capture processes. However, adsorbents designed to strongly bind CO<sub>2</sub> nearly always bind H<sub>2</sub>O strongly (sometimes even more so). Given that water is present in significant quantities in the inlet streams of most carbon capture processes, a method that avoids H<sub>2</sub>O competition for the CO<sub>2</sub> binding sites would be technologically valuable. In this paper, we consider a novel core-shell MOF design strategy, where a high-CO<sub>2</sub>-capacity MOF "core" is protected from competitive H<sub>2</sub>O-binding <i>via</i> a MOF "shell" that has very slow water diffusion. We consider a high-frequency adsorption/desorption cycle that regenerates the adsorbents before water can pass through the shell and enter the core. To identify optimal core-shell MOF pairs, we use a combination of experimental measurements, computational modeling, and multiphysics modeling. Our library of MOFs is created from two starting MOFs-UiO-66 and UiO-67-augmented with 30 possible functional group variations, yielding 1740 possible core-shell MOF pairs. After defining a performance score to rank these pairs, we identified 10 core-shell MOF candidates that significantly outperform any of the MOFs functioning alone. Core (optical fiber) ; Shell (structure) ; Materials science ; Nanotechnology ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Jincheng Xiong | Huixia Zhang | Linqian Qin | Shuai Zhang | Jiyue Cao | Haiyang Jiang
Source: International Journal of Molecular Sciences (review, 2022-04-07)
Field: Materials Science — Materials Chemistry

Magnetic Fluorescent Quantum Dots Nanocomposites in Food Contaminants Analysis: Current Challenges and Opportunities The presence of food contaminants can cause foodborne illnesses, posing a severe threat to human health. Therefore, a rapid, sensitive, and convenient method for monitoring food contaminants is eagerly needed. The complex matrix interferences of food samples and poor performance of existing sensing probes bring significant challenges to improving detection performances. Nanocomposites with multifunctional features provide a solution to these problems. The combination of the superior characteristics of magnetic nanoparticles (MNPs) and quantum dots (QDs) to fabricate magnetic fluorescent quantum dots (MNPs@QDs) nanocomposites are regarded as an ideal multifunctional probe for food contaminants analysis. The high-efficiency pretreatment and rapid fluorescence detection are concurrently integrated into one sensing platform using MNPs@QDs nanocomposites. In this review, the contemporary synthetic strategies to fabricate MNPs@QDs, including hetero-crystalline growth, template embedding, layer-by-layer assembly, microemulsion technique, and one-pot method, are described in detail, and their advantages and limitations are discussed. The recent advances of MNPs@QDs nanocomposites in detecting metal ions, foodborne pathogens, toxins, pesticides, antibiotics, and illegal additives are comprehensively introduced from the perspectives of modes and detection performances. The review ends with current challenges and opportunities in practical applications and prospects in food contaminants analysis, aiming to promote the enthusiasm for multifunctional sensing platform research. Quantum dot ; Nanotechnology ; Nanocomposite ; Magnetic nanoparticles ; Materials science ; Nanoparticle Carbon and Quantum Dots Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques