jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nisha Mehta | Jan M. L. Martin
Source: Physical Chemistry Chemical Physics (preprint, 2024-07-31)
Field: Materials Science — Materials Chemistry

Exploring the influence of (<i>n</i> − 1)d subvalence correlation and of spin–orbit coupling on chalcogen bonding This article presents a comprehensive computational investigation into chalcogen bonding interactions, focusing specifically on elucidating the role of subvalence (<i>n</i> - 1)d and (<i>n</i> - 1)sp correlation. The incorporation of inner-shell (<i>n</i> - 1)d correlation leads to a decrease in interaction energies for chalcogen-bonded systems (at least those studied herein), contradicting the observations regarding halogen bonding documented by Kesharwani <i>et al.</i> in <i>J. Phys. Chem. A</i>, <b>2018</b>, 122 (8), 2184-2197. The significance of (<i>n</i> - 1)sp subvalence correlation appears to be lower by an order of magnitude. Notably, among the various components of interaction energies computed at the PNO-LCCSD(T) or DF-CCSD levels, we identify the PNO-LMP2 or DF-MP2 component of the (<i>n</i> - 1)d correlation as predominant. Furthermore, we delve into the impact of second-order spin-orbit coupling (SOC2) on these interactions. The SOC2 effects appear to be less significant than the (<i>n</i> - 1)d correlation; however, they remain non-trivial, particularly for Te complexes. For the Se complexes, SOC2 is much less important. Generally, SOC2 stabilizes monomers more than dimers, resulting in reduced binding of the latter. Notably, at equilibrium and stretched geometries, SOC2 and (<i>n</i> - 1)d destabilize the complex; however, at compressed geometries, they exhibit opposing effects, with (<i>n</i> - 1)d becoming stabilizing. Chalcogen ; Coupling (piping) ; Spin–orbit interaction ; Condensed matter physics ; Spin (aerodynamics) ; Materials science ; Crystallography ; Chemical physics ; Physics ; Chemistry ; Metallurgy ; Thermodynamics Solid-state spectroscopy and crystallography ; Molecular spectroscopy and chirality ; Inorganic Chemistry and Materials
Authors: Akshay Mehta | Alkesh Yadav | Aman Kumar | Kanika Kanika | Manish
Source: International Journal of Advanced Biochemistry Research (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Role of nanotechnology in horticulture: An overview Nanotechnology, a cutting-edge field, has gained prominence in horticulture for its transformative impact on crop production, plant health, and resource management. Nanoparticles, due to their unique properties, offer innovative solutions in crop improvement and protection. Nanomaterials such as nanofertilizers enhance nutrient uptake efficiency, reducing environmental impact and optimizing resource utilization. Similarly, nanopesticides exhibit increased efficacy in pest and disease management, reducing the need for conventional chemical treatments. Nanotechnology also plays a pivotal role in plant growth regulation. Nanoscale delivery systems enable controlled release of growth regulators, facilitating precise modulation of plant development and flowering. This targeted approach enhances crop yield and quality while minimizing environmental repercussions. Furthermore, nanosensors contribute to real-time monitoring of environmental parameters, offering valuable insights for precision agriculture. Nanomaterial-based sensors detect changes in soil moisture, nutrient levels, and plant health, enabling timely interventions for optimal crop management. Despite the promising applications, the integration of nanotechnology in horticulture faces challenges related to environmental impact, ethical considerations, and regulatory frameworks. Addressing these concerns is crucial to ensure responsible and sustainable deployment of nanotechnology in agriculture. The role of nanotechnology in horticulture is dynamic and expansive. From enhancing nutrient management to revolutionizing pest control and growth regulation, nanotechnology holds immense potential for advancing sustainable and efficient practices in horticultural systems. However, a balanced approach is essential, considering ethical, environmental, and regulatory aspects to harness the full benefits while mitigating potential risks associated with nanotechnology in horticulture. The present review focuses on the Role of Nanotechnology in Horticulture. Nanotechnology ; Engineering ; Materials science Nanoparticles: synthesis and applications
Authors: Eann A. Patterson | Richard Taylor
Source: Royal Society Open Science (review, 2024-05-01)
Field: Materials Science — Materials Chemistry

The commoditization of civil nuclear power The commoditization of nuclear power through the factory production of sealed micro-power units within a digitally enabled holistic assurance framework is described. This would revolutionize nuclear power-plant design, construction, operation and decommissioning through a paradigm shift to manufacture-operate-remove-recycle (MORR). The potential impact of recent research on an integrated nuclear digital environment for large bespoke nuclear power plants and the design, build and operation of fusion power plants using such a digital environment is explored. These strands are interwoven to discuss the technical, economic and socio-political implications of MORR in the context of micro-reactors and to consider the potential evolution of safeguarding issues based on a digital assurance framework that leads to type approvals. Commoditization of nuclear power would lower costs in line with offshore wind and the output from a single production line in a factory could replace a third of current fossil fuel-based electricity generation in the UK over a 15-year period, making a significant contribution to achieving zero greenhouse gas emissions. The challenges associated with the changes in culture, both in the nuclear industry and in society, as well as the technology gaps, that need to be addressed in realizing this paradigm shift are identified and discussed. Commoditization ; Nuclear decommissioning ; Nuclear power ; Bespoke ; Nuclear weapon ; Factory (object-oriented programming) ; Engineering ; Environmental economics ; Manufacturing engineering ; Systems engineering ; Computer science ; Business ; Waste management ; Economics Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Risk Perception and Management
Authors: L. Arul Pragasan | M. Kalaiselvi
Source: Asian Journal of Research in Agriculture and Forestry (article, 2024-06-17)
Field: Chemistry — Inorganic Chemistry

Carbon Stock Potential of Coconut Plantations in University Campus: A Case Study from Coimbatore, India The urgent need to mitigate climate change has prompted researchers to explore various avenues for carbon sequestration. One such avenue is the utilization of natural ecosystems like forests and plantations. Coconut plantations in university campuses play a vital role in carbon sequestration in mitigation of climate change. In this case study, we report the potential of coconut plantation from Bharathiar University campus, India. Four plots of 50 m × 50 m were randomly sampled from the coconut plantation in Bharathiar University campus. Carbon stock of the coconut plantation was calculated based on field survey and allometric equations. A total of 171 trees were recorded from the four plots totalling one hectare (ha) area. The total carbon stock determined for the one ha of coconut plantation sampled from Bharathiar University campus was 31.69 tonne/ha and the mean carbon stock per tree was 0.19 ± 0.06 tonne/tree. We have discussed the carbon stock of coconut tree allocation by above-ground and below-ground parts. This case study highlights the importance of coconut plantations in university campuses in support of curbing the atmospheric carbon concentration. We suggest for plantation of coconut trees in university campuses, that can serve not only as carbon sinks but also contribute to fulfilling the sustainable development goals of the nation. Carbon stock ; Stock (firearms) ; Agroforestry ; Agricultural economics ; Forestry ; Business ; Environmental science ; Geography ; Economics ; Biology ; Ecology ; Archaeology ; Climate change Coconut Research and Applications
Authors: Kun Tan | Wenjie Hu | Oleksandr Shorinov | Yurong Wang
Source: Aerospace technic and technology (article, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simulating multi-particle deposition based on CEL method: studing the effects of particle and substrate temperature on deposition The subject matter of this study is to use numerical simulation methods to study the influence of the temperature of particles and substrates on the post-deposition coating during the multi-particle deposition process of cold spray. The goal is to study the temperature of Al6061 particles and the temperature of the substrate, which are factors that have a greater impact on the deposited coating, and to observe the shape of the coating and the temperature distribution of the cross-section of the substrate after deposition. The tasks to be solved are as follows: use Python scripts to model multi-particles, generate and randomly assign positions according to particle size distribution in the Euler domain, and establish a cold spray multi-particle collision model to simulate the process of cold spray deposition. The following methods were used: The influence of temperature and substrate temperature on the deposited coating was studied through a single variable method; the Coupled Eulerian Lagrangian (CEL) method was used to simulate the collision process of cold-sprayed Al6061 multi-particles. The following results were obtained: changing the temperature of Al6061 particles has a more obvious control effect on the porosity of the deposited coating; after particles of different temperatures impact the constant-temperature substrate, the high-temperature area on the surface of the substrate is mainly located at the junction of pits; after the particle temperature reaches 650K, the coating changes after deposition are no longer significant, indicating an optimal temperature range for Al6061 particle deposition; increasing the temperature of the substrate can increase the depth of particle deposition on the substrate; at the same time, it serves as a reference basis for further using the CEL method to predict the porosity of the Al6061 coating. Conclusions. The scientific novelty of the results obtained is as follows: 1) powder preheating can effectively reduce the porosity of Al6061 coating; 2) the CEL method has good robustness and is used to simulate cold spray multi-particle deposition to monitor the porosity of the coating, which cannot be achieved by the SPH and ALE methods. Particle deposition ; Deposition (geology) ; Materials science ; Particle (ecology) ; Substrate (aquarium) ; Chemical engineering ; Composite material ; Engineering ; Geology Copper Interconnects and Reliability ; Electron and X-Ray Spectroscopy Techniques
Authors: Lucie Cornehl | Pascal Gauweiler | Xiaorong Zheng | Julius Krause | Florian Schwander | Reinhard Töpfer | Robin Gruna | Anna Kicherer
Source: Frontiers in Plant Science (article, 2024-07-05)
Field: Chemistry — Analytical Chemistry

Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in <i>Vitis vinifera</i> (L.) varieties 'Chardonnay', 'Riesling', 'Dornfelder', and 'Pinot Noir'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions. Ripeness ; Malic acid ; Sugar ; Partial least squares regression ; Vineyard ; Wine ; Chemistry ; Viticulture ; Horticulture ; Vitis vinifera ; Fructose ; Mathematics ; Food science ; Environmental science ; Botany ; Citric acid ; Statistics ; Biology ; Ripening Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis
Authors: Enes K. Ergin | Junia J.K. Myung | Philipp F. Lange
Source: Journal of Proteome Research (article, 2024-05-28)
Field: Chemistry — Spectroscopy

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport. Equivalence (formal languages) ; Computational biology ; Proteome ; Statistical hypothesis testing ; Computer science ; Set (abstract data type) ; Biology ; Statistical analysis ; Bioinformatics ; Biological system ; Mathematics ; Statistics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Protein Structure and Dynamics
Authors: Yahao Li | Enyu Jiang | Kaijia Hu | Yongqian Peng | Ziqi Ni | Fengqi Liu | Yicong Ye | Shun Li | Shuxin Bai
Source: Scripta Materialia (article, 2024-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Active learning-enabled the discovery of ultra-high saturation magnetization soft magnetic alloys Materials science ; Magnetization ; Saturation (graph theory) ; Condensed matter physics ; Metallurgy ; Magnetic field Magnetic Properties and Applications ; Magnetic Properties of Alloys ; Shape Memory Alloy Transformations
Authors: Jiayi Du | Jun Guo | Wei Liu | Ziwei Li | Gang Huang | Xinbo Zhang
Source: Science China Chemistry (article, 2024-09-29)
Field: Chemistry — Electrochemistry

A universal strategy of multi-objective active learning to accelerate the discovery of organic electrode molecules Electrode ; Organic molecules ; Molecule ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Combinatorial chemistry ; Organic chemistry Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Baolei Li | Da Wang | Miao Yu | Chaozheng He | Xue Li | Jing Zhai | Mdmahadi Hasan | Chenxu Zhao | Min Wang | Dingcai Shen
Source: Chinese Chemical Letters (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Accelerating multi-objective catalytic material design: A model-based method Catalysis ; Computer science ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Hao Deng | Bin Liu
Source: The Journal of Physical Chemistry Letters (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with data sets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics (MD) simulations to predict wide-ranging thermodynamic, structural, and vibrational properties. The simulated phase diagram (500-1400 K and 0-16 GPa) agrees with experimental measurements. The SGP-based MD simulations also successfully predicted that the B13 defect is critical in stabilizing β-B below 700 K. At higher temperatures, the entropy becomes the dominant factor, making β-B the more stable phase over α-B. This letter demonstrates that SGP potentials based on a training set consisting of defect-free-only systems could make correct predictions of defect-related phenomena in solid-state crystals, paving the path to investigate crystal phase stability and transitions. Stability (learning theory) ; Boron ; Bayesian probability ; Interatomic potential ; Phase (matter) ; Materials science ; Computer science ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Chemistry ; Computational chemistry ; Molecular dynamics ; Nuclear physics ; Quantum mechanics Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Shinyoung Bae | Dongjae Shin | Haechang Kim | Jeong Woo Han | Jong Min Lee
Source: Journal of Chemical Theory and Computation (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search Numerous systematic methods have been developed to search for the global minimum of the potential energy surface, which corresponds to the optimal atomic structure. However, the majority of them still demand a substantial computing load due to the relaxation process that is embedded as an inner step inside the algorithm. Here, we propose a hybrid approach that combines Bayesian optimization (BO) and a local search that circumvents the relaxation step and efficiently finds the optimum structure, particularly in supported metal systems. The hybridization strategy combining the capabilities of BO's effective exploration and the local search's fast convergence expedites structural search. In addition, the formulation of physical constraints regarding the materials system and the feature of screening structure similarity enhance the computational efficiency of the proposed method. The proposed algorithm is demonstrated in two supported metal systems, showing the potential of the proposed method in the field of structural optimization. Computer science ; Local search (optimization) ; Convergence (economics) ; Relaxation (psychology) ; Mathematical optimization ; Bayesian optimization ; Search algorithm ; Process (computing) ; Global optimization ; Field (mathematics) ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: H. Ahmed | Xiaoping Zeng | Yang Wang | Hilal Bello | Nayyar Iqbal | Rosdiadee Nordin
Source: Advanced Optical Materials (article, 2024-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Metasurface Polarizers Synthesis Using Effective Data Generation with Adaptive Attention and Space‐To‐Depth Enhanced Network Abstract Conventional approaches for designing metasurfaces with the desired scattering response are often a time‐consuming and onerous process that heavily relies on the design experience and empirical methods. In this paper, a time and resource‐efficient design method is proposed for the synthesis of multiple metasurface polarizers with an efficient automated data generation process by utilizing the structure validation surrogate model (SVSM). Moreover, a novel attention and space‐to‐depth enhanced adaptive tandem network (ASE‐ATN) is proposed which integrates symmetry‐aware space‐to‐depth (SA‐SPD) along with the attention mechanism for efficient feature extraction. Several design examples of metasurfaces with linear polarization to orthogonal linear polarization (LP‐OLP) and/or linear polarization to circular polarization (LP‐CP) conversion capabilities are provided to verify the performance of the proposed method. The feasibility of the proposed method is demonstrated through the fabrication and measurement of a dual‐band dual‐polarization converter. Polarizer ; Materials science ; Polarization (electrochemistry) ; Computer science ; Space mapping ; Electronic engineering ; Optics ; Algorithm ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis