jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bruno C. Batista | Elena Romanovskaia | Valentin Romanovski | Emmanuel Michael | James T. Burns | Ji Ma | István Z. Kiss | John R. Scully | Oliver Steinbock
Source: Advanced Science (article, 2024-08-19)
Field: Materials Science — Biomaterials

Morphogenic Modeling of Corrosion Reveals Complex Effects of Intermetallic Particles Corrosion processes are often discussed as stochastic events. Here, it is shown that some of these seemingly random processes are not driven by nanoscopic fluctuations but rather by the spatial distribution of micrometer-scale heterogeneities that trigger fast reactions associated with corrosion. Using a novel excitable reaction-diffusion model, corrosion waves traveling over the metal surface and the associated material loss are described. This resulting nonuniform corrosion penetration, seen as a height loss in modeling, exposes buried intermetallic particles, which depending on the local electrochemical state of the surface trigger or block new waves. Informed by quantitative experimental data for the Mg-Al-Zn alloy AZ31B, wave speeds, wave widths, and average material loss are accurately captured. Morphogenic mitigation based on wave-breaking microparticles is also simulated. While AZ31B corrosion is identified as a process driven by rare-wave events, this study predicts several other corrosion regimes that proceed via spots or patchy patterns, opening the door for new protection, design, and prediction strategies. Intermetallic ; Corrosion ; Materials science ; Metallurgy ; Chemical engineering ; Engineering ; Alloy Magnesium Alloys: Properties and Applications ; Material Properties and Failure Mechanisms ; Material Properties and Applications
Authors: Bingjia Yang | Pinchen Xie | Roberto Car
Source: npj Computational Materials (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Deuteration removes quantum dipolar defects from KDP crystals Abstract Dielectric properties of the hydrogen-bonded ferroelectric crystal KH 2 PO 4 (KDP) differ significantly from those of KD 2 PO 4 (DKDP). It is well established that deuteration affects the interplay of hydrogen-bond switches and heavy ion displacements that underlie the emergence of macroscopic polarization, but a detailed microscopic model is missing. We show that all-atom path integral molecular dynamics simulations can predict the isotope effects, revealing the microscopic mechanism that differentiates KDP and DKDP. Proton tunneling generates phosphate configurations that do not contribute to the polarization. At low temperatures, these quantum dipolar defects are substantial in KDP but negligible in DKDP. These intrinsic defects explain why KDP has lower spontaneous polarization and transition entropy than DKDP. The prominent role of quantum fluctuations in KDP is related to the unusual strength of the hydrogen bonds and should be equally important in other crystals of the KDP family, which exhibit similar isotope effects. Dipole ; Ferroelectricity ; Polarization (electrochemistry) ; Dielectric ; Quantum tunnelling ; Kinetic isotope effect ; Hydrogen bond ; Chemistry ; Ion ; Isotope ; Chemical physics ; Condensed matter physics ; Quantum ; Hydrogen atom ; Molecular physics ; Materials science ; Atomic physics ; Molecule ; Deuterium ; Physics ; Physical chemistry ; Optoelectronics Solid-state spectroscopy and crystallography ; Photorefractive and Nonlinear Optics ; Acoustic Wave Resonator Technologies
Authors: Younes Gholamzadeh | Abdolhossein Hemmati‐Sarapardeh | Mohammad Sharifi
Source: Scientific Reports (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Interfacial tension reduction using nitrogen graphene quantum dots with various precursors, molar ratios, and synthesis durations for enhanced oil recovery Nanofluids have the capacity to reduce interfacial tension (IFT) of crude oil and water for enhanced oil recovery (EOR) operations, but traditional nanoparticles are limited in tight reservoirs due to their inappropriate size for micro-nano pores and their tendency to aggregate. In this paper, Graphene Quantum Dots (GQDs) with simple and favorable properties are developed, and their performance and mechanism for reducing IFT are evaluated. The paper also aims to explore the effects of GQD precursor type, synthesis duration, and molar percentages of precursors on reducing IFT. For this purpose, citric acid was used as a carbon source, and ethylenediamine, urea, and thiourea were used as nitrogen sources to synthesize different GQDs. FTIR, XPS, HR-TEM, XRD, UV visible, and PL photoluminescence were used to identify the GQDs' characteristics. The highest IFT reduction value is achieved by using 1000 ppm ethylenediamine-derived GQDs, which reduces the IFT from 19.03 to 0.70 mN/m at 200,000 ppm NaCl concentrations without using any surfactants. The XPS analysis revealed that ethylenediamine-derived GQDs exhibit higher pyrrolic nitrogen content and a relative intensity ratio of sp3 C/sp2 C. It has been identified that the presence of more polar atomic bonds on the surfaces of GQDs decreases the ability of GQDs to reduce IFT. In this way, urea-derived and thiourea-derived GQDs exhibit less capability for IFT reductions from 19.03 to 12.33 and 18.04 mN/m at 1000 ppm GQDs and 200,000 ppm NaCl concentration, respectively. Furthermore, ethylenediamine-derived and urea-derived GQDs perform optimally at approximately 5 and 10 h, respectively, with a precursor molar ratio of 3 (mole ethylenediamine/urea per mole citric acid), while thiourea-derived GQDs show no significant IFT change with varying synthesis times or precursor molar ratio. The developed GQDs provide a promising solution for EOR operations in unconventional reservoirs by significantly decreasing the IFT between crude oil and nanofluids. Ethylenediamine ; Graphene ; X-ray photoelectron spectroscopy ; Thiourea ; Urea ; Fourier transform infrared spectroscopy ; Materials science ; Chemical engineering ; Quantum dot ; Nanoparticle ; Graphene quantum dot ; Chemistry ; Citric acid ; Nuclear chemistry ; Inorganic chemistry ; Nanotechnology ; Organic chemistry Carbon and Quantum Dots Applications ; ZnO doping and properties ; Graphene research and applications
Authors: Cedric Devos | Christos Xiouras | Tom Van Gerven | Simon Kuhn
Source: Crystal Growth & Design (article, 2024-03-12)
Field: Materials Science — Materials Chemistry

Unraveling the Role of Fluid Shear on Primary Nucleation of Paracetamol through an Energy-Dissipation Rate Analysis The effect of fluid shear on the primary nucleation of paracetamol in water, isopropyl alcohol, and acetonitrile was investigated. First, experiments were performed with a Taylor–Couette crystallizer. For all three solvents, a comparable effect of fluid shear on the nucleation kinetics was found: increasing fluid shear results in an increase in the nucleation rate and a decrease in the variability of the induction times. The experimental analysis shows that the mean induction times are dependent on the energy dissipation rate to a power of approximately −1/3. A comparative investigation for paracetamol crystallization in stirred batch crystallizers revealed the same dependence of mean induction times on the energy dissipation rate. Extending this analysis to experimental data sets from the literature confirms this further. To find a physical meaning for this dependency, the results are interpreted in terms of the shear-enhanced mass transfer and shear-enhanced cluster aggregation theories. Nucleation ; Shear rate ; Thermodynamics ; Chemistry ; Dissipation ; Shear (geology) ; Crystallization ; Materials science ; Organic chemistry ; Rheology ; Composite material ; Physics Crystallization and Solubility Studies ; nanoparticles nucleation surface interactions ; Material Dynamics and Properties
Authors: Xiang Yan | Chiranjeevi Kanike | Qiuyun Lu | Yanan Li | Hongyan Wu | Vida Dehghan Niestanak | Nobuo Maeda | Arnab Atta | Larry D. Unsworth | Xuehua Zhang
Source: ACS Applied Materials & Interfaces (article, 2024-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Streamlined Flow Synthesis of Plasmonic Nanoparticles and SERS Detection of Uremic Toxins with Trace-Level Liquid Volumes in a Microchamber Rapid detection of uremic toxins is crucial due to their severe health risks, including oxidative stress, inflammation, neurotoxicity, cardiovascular complications, and progression of chronic kidney disease. Surface-enhanced Raman spectroscopy (SERS) may provide sensitive, fast, and clinical-grade real-time monitoring of these toxins, enabling effective management with timely dialysis treatments. This study introduces a 3D-printed microchamber that integrates the fabrication of plasmonic metal nanoparticles for the in-flow detection of biological toxins and pharmaceutical drugs using SERS, making it ideal for on-site diagnostics in clinical settings. The microchamber supports quantitative and highly reproducible detection with liquid volumes under 100 μL, which is crucial for trace-level biomarker detection and minimizing cross-contamination. It employs a tunable solvent exchange method for the in situ synthesis of silver nanoparticles (AgNPs) on flexible PDMS or rigid Si wafer substrates, avoiding costly nanofabrication techniques. Ultralow detection limits were achieved for two model compounds and three pharmaceutical drugs: 10<sup>-11</sup> M for rhodamine 6G, 10<sup>-7</sup> M for adenine, and 10<sup>-6</sup> M for the pharmaceutical drugs. A total of 13 biological toxins, including three neurotransmitters, one neuromodulator, five amino acids, two polyamines, and two urea cycle metabolites, were detected with quantitative limits ranging from 10<sup>-3</sup> to 10<sup>-6</sup> M, all below permissible levels and aligning with physiological conditions. SERS detection within microchambers facilitates rapid on-site analysis, proving ideal for personalized health monitoring, point-of-care diagnostics, and environmental pollution assessment. Materials science ; Plasmon ; Nanotechnology ; Nanoparticle ; Uremic toxins ; Plasmonic nanoparticles ; TRACE (psycholinguistics) ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Electrohydrodynamics and Fluid Dynamics
Authors: Keqiang Yan | Cong Fu | Xiaofeng Qian | Xiaoning Qian | Shuiwang Ji
Source: arXiv (Cornell University) (preprint, 2024-03-18)
Field: Materials Science — Materials Chemistry

Complete and Efficient Graph Transformers for Crystal Material Property Prediction Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants; namely, iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). Transformer ; Property (philosophy) ; Graph ; Computer science ; Theoretical computer science ; Engineering ; Electrical engineering ; Philosophy ; Voltage Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yeonjoon Kim | Jae‐Young Cho | Hojin Jung | Lydia Meyer | Gina M. Fioroni | Christopher D. Stubbs | Keunhong Jeong | Robert L. McCormick | Peter C. St. John | Seonah Kim
Source: Green Chemistry (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Designing green chemicals by predicting vaporization properties using explainable graph attention networks Computational predictions of vaporization properties aid the de novo design of green chemicals, including clean alternative fuels, working fluids for efficient thermal energy recovery, and polymers that are easily degradable and recyclable. Vaporization ; Biochemical engineering ; Graph ; Computer science ; Process engineering ; Thermal ; Polymer ; Chemistry ; Environmental science ; Organic chemistry ; Thermodynamics ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysis and Oxidation Reactions
Authors: Xiaoling Zheng | Yong Xie | Zhuoer Chen | Mingdong Cao | Xianlu Lei | Tao Le
Source: Food Chemistry X (review, 2024-10-28)
Field: Chemistry — Analytical Chemistry

A comprehensive review on the pretreatment and detection methods of nitrofurans and their metabolites in animal-derived food and environmental samples In recent years, the residues of nitrofurans (NFs) and their metabolites in animal-derived food and environmental samples have gained widespread attention. The parent drugs and their metabolites have displayed significant toxicity to human health including carcinogenic, mutagenic and teratogenic effects, leading to banned in animal husbandry in many countries. Hence, monitoring the residues of NFs is necessary to guarantee public health and ecological security. This review aims to summarize and assess the structural properties, residue status, sample pretreatment methods (liquid-liquid extraction, solid-phase extraction, Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS), and field-assisted extraction), and detection methods (chromatographic analysis, immunoassay, and some innovative detection methods) for NFs and their metabolites in animal-derived food and environmental samples. This paper provides a detailed reference and discussion for the analysis of NFs and their metabolites, which can effectively promote the establishment of innovative detection methods for NFs and their metabolites residues. Nitrofuran ; Animal food ; Biotechnology ; Chemistry ; Environmental chemistry ; Biochemical engineering ; Food science ; Biology ; Engineering ; Genetics Dye analysis and toxicity ; Isotope Analysis in Ecology ; Pesticide Residue Analysis and Safety
Authors: L. Freire | Ignacio Ezpeleta | Julio C. Sánchez | Rubén García Castro
Source: Metals (article, 2024-02-27)
Field: Materials Science — Materials Chemistry

Advanced EIS-Based Sensor for Online Corrosion and Scaling Monitoring in Pipelines of Geothermal Power Plants Corrosion and scaling in metal pipelines are the major issues in the exploitation of geothermal sources. Geothermal fluids are complex mixtures consisting of dissolved gases and high-salinity solutions. This creates very aggressive environments primarily due to the high concentrations of carbon dioxide (CO2), hydrogen sulfide (H2S), chlorides, and other chemical species. Besides, the high temperature of the brines also increases corrosion rates, which can lead to failures related to stress and fatigue corrosion. On the other hand, reinjection of cooled brine exiting the heat exchanger favors the onset of scaling, since the chemicals dissolved in geothermal waters may tend to precipitate promoting inorganic depositions on the casing. Corrosion and scaling phenomena are difficult to detect visually or monitor continuously. Standard techniques based on pH, temperature pressure, electrical resistance measurements, chemistry composition, and physical properties are habitually applied as indirect methods for corrosion rate control. These methods, however, lack enough robustness for accurate and reliable measuring of the corrosion behavior of materials. To address this issue, a novel system has been proposed for the continuous monitoring of corrosion degradation caused by the effect of the geothermal brines. The present work aims to design, develop, and validate a dedicated electrochemical-based test system for online and onsite monitoring of the corrosion rate and scaling growth occurring on different materials exposed to real operating conditions. This system uses non-standard methods based on electrochemical impedance spectroscopy (EIS) to obtain quantitative data related to the material quality. It can be used to track the condition of the pipeline, reducing the operation and maintenance (O&amp;M) costs and shutdown times. By providing early corrosion rate data, this system allows the prediction of failures in critical units of the plant. Corrosion ; Geothermal gradient ; Brine ; Geothermal power ; Pipeline transport ; Scaling ; Environmental science ; Dielectric spectroscopy ; Passivation ; Petroleum engineering ; Produced water ; Materials science ; Metallurgy ; Environmental engineering ; Geothermal energy ; Geology ; Electrochemistry ; Chemistry ; Composite material Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sonja Wieland | Abdurrahman Ali El Yumin | Simon Settele | Jana Zaumseil
Source: The Journal of Physical Chemistry C (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Photo-Activated, Solid-State Introduction of Luminescent Oxygen Defects into Semiconducting Single-Walled Carbon Nanotubes Oxygen defects in semiconducting single-walled carbon nanotubes (SWCNTs) are localized disruptions in the carbon lattice caused by the formation of epoxy or ether groups, commonly through wet-chemical reactions. The associated modifications of the electronic structure can result in luminescent states with emission energies below those of pristine SWCNTs in the near-infrared range, which makes them promising candidates for applications in biosensing and as single-photon emitters. Here, we demonstrate the controlled introduction of luminescent oxygen defects into networks of monochiral (6,5) SWCNTs using a solid-state photocatalytic approach. UV irradiation of SWCNTs on the photoreactive surfaces of the transition metal oxides TiO<sub><i>x</i></sub> and ZnO<sub><i>x</i></sub> in the presence of trace amounts of water and oxygen results in the creation of reactive oxygen species that initiate radical reactions with the carbon lattice and the formation of oxygen defects. The created ether-d and epoxide-l defect configurations give rise to two distinct red-shifted emissive features. The chemical and dielectric properties of the photoactive oxides influence the final defect emission properties, with oxygen-functionalized SWCNTs on TiO<sub><i>x</i></sub> substrates being brighter than those on ZnO<sub><i>x</i></sub> or pristine SWCNTs on glass. The photoinduced functionalization of nanotubes is further employed to create lateral patterns of oxygen defects in (6,5) SWCNT networks with micrometer resolution and thus spatially controlled defect emission. Carbon nanotube ; Materials science ; Luminescence ; Oxygen ; Photochemistry ; Nanotechnology ; Graphene ; Carbon fibers ; Surface modification ; Chemical engineering ; Chemistry ; Optoelectronics ; Organic chemistry ; Composite material Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors ; Microbial Fuel Cells and Bioremediation
Authors: Jacek Karolczak | Anna Przybyłowska | Konrad Szewczyk | Witold Taisner | John M. Heumann | Michael H. B. Stowell | Michał Nowicki | Dariusz Brzeziński
Source: Bioinformatics (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Ligand identification in CryoEM and X-ray maps using deep learning Code and model weights are available on GitHub at https://github.com/jkarolczak/ligands-classification. An accompanying ChimeraX bundle is available at https://github.com/wtaisner/chimerax-ligand-recognizer. Computer science ; Source code ; Identification (biology) ; Artificial intelligence ; Software ; Feature (linguistics) ; Deep learning ; Algorithm ; Cryo-electron microscopy ; Machine learning ; Pattern recognition (psychology) ; Physics ; Biology ; Nuclear magnetic resonance ; Programming language Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Enzyme Structure and Function
Authors: Mostafa Khajeh | Mansour Ghaffari‐Moghaddam | Jamshid Piri | Afsaneh Barkhordar | Turgut Öztürk
Source: Scientific Reports (article, 2024-10-29)
Field: Chemistry — Spectroscopy

Machine learning-aided enhancement of white tea extraction efficiency using hybridized GMDH models in microwave-assisted extraction White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC<sub>50</sub> (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC<sub>50</sub> and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes. Extraction (chemistry) ; Computer science ; Microwave ; Artificial intelligence ; Machine learning ; Chromatography ; Chemistry ; Telecommunications Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Fermentation and Sensory Analysis