jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Faranak Hasanpour | Saber Zahri | Arash Abdolmaleki | Asadollah Asadi
Source: Journal of Ardabil University of Medical Sciences (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Synthesis and Characterization of Polycaprolactone Scaffolds Containing Carbon Quantum Dots Nanoparticles for Tissue Engineering Applications Background: Tissue engineering, by designing biological scaffolds and imitating the extracellular environment, helps the growth and proliferation of cells and plays a key role in replacing and repairing damaged tissues.In recent years, the addition of nanoparticles, such as carbon quantum dots, to biological scaffolds has received attention.In this research, the synthesis of polycaprolactone scaffolds containing carbon quantum dots and the investigation of biocompatibility effects and their protection have been discussed.Methods: Carbon quantum dots were synthesized using the pyrolysis method, and polymer scaffolds containing carbon quantum dots were prepared by the electrospinning method.The physical and chemical properties of the scaffold were evaluated by scanning electron microscopy and FTIR spectroscopy.The scaffolds' biocompatibility and antioxidant properties were measured by the MTT method.Results: Examination of the morphology and chemical showed the appropriate porosity of the scaffold containing carbon quantum dots.The MTT assay significantly enhanced stem cell viability on scaffolds incorporating carbon quantum dots.Furthermore, these scaffolds exhibited a significant protective effect against oxidative stress.Conclusion: This study showed that the polycaprolactone scaffold containing carbon quantum dots, with high biocompatibility and suitable antioxidant properties, provides an effective substrate for tissue engineering and cell protection under oxidative stress conditions. Polycaprolactone ; Characterization (materials science) ; Quantum dot ; Nanoparticle ; Nanotechnology ; Materials science ; Tissue engineering ; Biomedical engineering ; Engineering ; Composite material ; Polymer Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Justin Sing | Joshua Charkow | Mohammed AlHigaylan | Ira Horecka | Leon L. Xu | Hannes Röst
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-17)
Field: Chemistry — Spectroscopy

MassDash: A Web-based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization Abstract With the increased usage, diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform, DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity providing additional insights into the data. The modular framework is easily extendable enabling rapid algorithm development of novel peak picker techniques, such as deep learning based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash , and a demo version can be accessed at https://massdash.streamlit.app . Python (programming language) ; Computer science ; Visualization ; MIT License ; Software ; Modular design ; Web application ; Graphical user interface ; Data visualization ; Data mining ; Human–computer interaction ; World Wide Web ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Stéphanie Bessette | Raynald Gauvin
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Towards 3D quantitative imaging in FIB-SEM for applications in battery materials Materials science ; Battery (electricity) ; Nanotechnology ; Computer science ; Systems engineering ; Engineering ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Bikash Kanungo | Soumi Tribedi | Paul M. Zimmerman | Vikram Gavini
Source: arXiv (Cornell University) (preprint, 2024-08-05)
Field: Chemistry — Spectroscopy

Accelerating inverse Kohn-Sham calculations using reduced density matrices The Ryabinkin-Kohut-Staroverov (RKS) and Kanungo-Zimmerman-Gavini (KZG) methods offer two approaches to find exchange-correlation (XC) potentials from ground state densities. The RKS method utilizes the one- and two-particle reduced density matrices to alleviate any numerical artifacts stemming from a finite basis (e.g., Gaussian- or Slater-type orbitals). The KZG approach relies solely on the density to find the XC potential, by combining a systematically convergent finite-element basis with appropriate asymptotic correction on the target density. The RKS method, being designed for a finite basis, offers computational efficiency. The KZG method, using a complete basis, provides higher accuracy. In this work, we combine both the methods to simultaneously afford accuracy and efficiency. In particular, we use the RKS solution as initial guess to the KZG method to attain a significant $3-11\times$ speedup. This work also presents a direct comparison of the XC potentials from the RKS and the KZG method and their relative accuracy on various weakly and strongly correlated molecules, using their ground state solutions from accurate configuration interaction calculations solved in a Slater orbital basis. Kohn–Sham equations ; Inverse ; Physics ; Density functional theory ; Mathematics ; Quantum mechanics ; Geometry Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Advanced Condensed Matter Physics
Authors: Simrith Pulicharla
Source: International Journal of Innovative Research in Science Engineering and Technology (article, 2024-01-30)
Field: Materials Science — Biomaterials

Overcoming the Blood-Brain Barrier for Targeted Drug Delivery in Neurological Diseases An Overview of the Blood-Brain Barrier - The blood-brain barrier (BBB) is an extremely selective protective barrier between the brain &amp; the bloodstream. The BBB is made up of specialized endothelial cells, tight junctions, and other cellular components. The BBB works as a filtering mechanism that prevents harmful substances, like pathogens &amp; toxins from entering the brain. This protective barrier is absolutely crucial to the brain as the BBB helps it maintain a stable environment that is extremely sensitive to things like fluctuations in chemicals and other external factors like temperature changes or hormonal fluctuations. But these same properties that make the BBB so effective at what it does also creates a significant challenge for medical treatments that need to reach the brain. The BBB very effectively blocks the passage of drugs, especially large molecules &amp; therapeutic agents that could help treat neurological diseases like Alzheimer’s, Parkinson’s, or brain tumors. Blood–brain barrier ; Drug ; Computer science ; Medicine ; Drug delivery ; Intensive care medicine ; Pharmacology ; Central nervous system ; Internal medicine ; Nanotechnology Nanoparticle-Based Drug Delivery ; Apelin-related biomedical research ; Adenosine and Purinergic Signaling
Source: International Journal of Veterinary Science (article, 2024-01-01)
Field: Materials Science — Biomaterials

Poly Lactic-co-Glycolic Acid Nanoparticles for Drug Delivery PLGA nanoparticles are seen as very useful for delivering medicine, and they can make treatments work better.This book chapter looks at how PLGA nanoparticles are important in delivering drugs to the body.The background part explains drug delivery systems are important for making treatments work better.In this discussion, we focus on PLGA nanoparticles play a very important role in using small technology to deliver medicine.This is because they are very safe for the body and can break down naturally, which makes medicine safer and more effective.In addition, PLGA nanoparticles can release drugs in a targeted way, which makes them more flexible and effective than other ways of delivering drugs.The PLGA nanoparticles are made and different techniques can be used to change their properties for different uses.Also, the paper looks at things like the amounts of different materials, how big the molecules are, substances that help keep everything steady, and liquids can change how nanoparticles behave and can hold medicine.We use different methods to check the quality and effectiveness of PLGA nanoparticles, like measuring their size and looking at their shape.In addition, drugs are put into capsules ways to measure how much drug is in a capsule, and things that affect drugs are put into capsules.We carefully study drugs released from PLGA nanoparticles in the lab and in biological settings to understand factors that can affect the process.In general, PLGA nanoparticles are commonly used to deliver drugs for different diseases including cancer, brain diseases, vaccines, and heart problems. Glycolic acid ; Lactic acid ; Drug delivery ; Drug ; Chemistry ; PLGA ; Pharmacology ; Nanoparticle ; Biochemistry ; Medicine ; Nanotechnology ; Materials science ; Organic chemistry ; Bacteria ; Biology ; In vitro biodegradable polymer synthesis and properties ; Advanced Drug Delivery Systems ; Seaweed-derived Bioactive Compounds
Authors: NOOR HAMID HAMEED | Rana A. Majed
Source: Iraqi journal of oil and gas researches (article, 2024-10-01)
Field: Chemistry — Analytical Chemistry

Applications of Artificial Neural Networks in the Petroleum Industry: A Review One of the most crucial phases in creating an accurate and ideal field development plan for oil and gas projects is evaluating the formation and characterizing the reservoir.Many different types of data are required for reservoir characterization and modeling, including wireline logs, LWD logs, SCAL data, geology and seismic data.Furthermore, sufficient understanding of the underlying physical interactions between each parameter.Recent developments in artificial intelligence and neural network-based petroleum exploration technology have opened up new possibilities for the sector in terms of reservoir characterization, that is more affordable, effective, and precise.Several research works pertaining to the application of artificial neural networks (ANNs) in the petroleum sector were examined, synthesized, and categorized into four main categories: applications in exploration, drilling, production, and reservoir engineering.An overview of applications of artificial neural networks in petroleum engineering was presented.An established process for using ANNs in any petroleum application was performed and displayed through a flowchart that may be used as a useful guide to implement ANNs for any petroleum application. Artificial neural network ; Petroleum ; Pipeline (software) ; Feature (linguistics) ; Automation Petroleum Processing and Analysis
Authors: Yuri Nascimento Nariyoshi | Ana Paula Meneguelo | Rejane de Castro Santana
Source: Latin American Journal of Energy Research (article, 2024-12-26)
Field: Chemistry — Organic Chemistry

Viscosity reduction of water-in-oil emulsion by phase inversion: surfactants performance and characterization The phase inversion technique has received attention in the petroleum industry in order to decrease energy consumption. Crude oil emulsions with model brine solutions containing 1.5% wt NaCl and 1.0 wt.% surfactants were produced and evaluated on electrical conductivity, kinetic stability, microstructure and rheology. It was found that phase inversion occurred in the range of 20-40 wt.% of brine. Emulsions having 10-40 wt.% of brine and Triton-X-100 manifested lower phase separation than without surfactant or with Tween 80, while oil-in-water emulsions having 50 and 60 wt.% of brine and Triton-X-100 achieved the lowest value of viscosity. Thus, these results elucidate the influence of selected parameters on emulsions properties aiming to improve mobility and flowability by phase inversion of high viscous water-in-oil emulsion. Phase inversion ; Emulsion ; Viscosity ; Materials science ; Oil viscosity ; Inversion (geology) ; Phase (matter) ; Characterization (materials science) ; Chemical engineering ; Chromatography ; Petroleum engineering ; Chemistry ; Composite material ; Geology ; Nanotechnology ; Engineering ; Organic chemistry Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques ; Pickering emulsions and particle stabilization
Authors: Xiaohui Hu | Yifei Cao | Peng Zhou | Yuqiang Wu | Tchalla Korohou
Source: Journal of Sensors (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Rapid and Nondestructive Evaluation of Rice SPAD Value under Disease Stress Using Hyperspectral Imaging Sensors Leaf chlorophyll content is an important indicator of photosynthetic capacity and health status and plays a key role in monitoring plant growth. At present, research on chlorophyll content monitoring has mainly focused on establishing a relationship between vegetation index and chlorophyll content, which was composed of several hyperspectral wavelengths, resulting in the loss of effective information contained in certain wavelengths strongly associated with disease. Therefore, it was very important to select spectral wavelengths to reveal the hyperspectral response of chlorophyll content in diseased leaves. Fifty groups of rice leaves infected with bacterial leaf blight were studied, and hyperspectral images and soil plant analysis development (SPAD) values of disease‐stressed rice leaves were collected every 5 days from the first day after inoculation. A method for predicting the SPAD value of diseased rice leaves using random forest (RF) combined with a back‐propagation optimized neural network (BPNN) was proposed. The bird swarm algorithm (BSA) and the immune algorithm (IA) were used to solve the problems of information loss, local minimization, and slow convergence of the BPNN. The BPNN, BSA–BPNN, IA–BPNN, and BSA–IA–BPNN prediction models for the SPAD value of diseased rice leaves are established. The results show that the BSA–IA–BPNN model with characteristic wavelengths selected by RF achieved the best prediction effect, and the prediction sets R 2 , root‐mean‐square error, and relative error are 0.97%, 4.34%, and 3.33% respectively. The shortest prediction modeling time is 1.54 s, and the model exhibits the best stability. This study provides allegorical support and a theoretical basis for achieving rapid, nondestructive detection of the SPAD value of diseased rice leaves, provides an effective reference for estimating the SPAD value of diseased rice leaves in the field, and is of great importance for rice disease monitoring. Hyperspectral imaging ; Nondestructive testing ; Stress (linguistics) ; Value (mathematics) ; Remote sensing ; Environmental science ; Materials science ; Engineering ; Computer science ; Medicine ; Geology ; Radiology ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Biosensors and Analytical Detection
Authors: G. T. Gray | Anurag Tiwari | Shane Bergsma | Joel Hestness
Source: arXiv (Cornell University) (preprint, 2024-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers Per-example gradient norms are a vital ingredient for estimating gradient noise scale (GNS) with minimal variance. Observing the tensor contractions required to compute them, we propose a method with minimal FLOPs in 3D or greater tensor regimes by simultaneously computing the norms while computing the parameter gradients. Using this method we are able to observe the GNS of different layers at higher accuracy than previously possible. We find that the total GNS of contemporary transformer models is predicted well by the GNS of only the normalization layers. As a result, focusing only on the normalization layer, we develop a custom kernel to compute the per-example gradient norms while performing the LayerNorm backward pass with zero throughput overhead. Tracking GNS on only those layers, we are able to guide a practical batch size schedule that reduces training time by 18% on a Chinchilla-optimal language model. Normalization (sociology) ; Transformer ; Computer science ; Engineering ; Electrical engineering ; Voltage Magnetic Properties and Applications ; Advancements in Semiconductor Devices and Circuit Design ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Cedric Dzidzor Kodjo Amengor | Prince Danan Biniyam | Patrick Gyan | Francis Klenam Kekessie | Benjamin Kingsley Harley
Source: Research Square (preprint, 2024-04-22)
Field: Chemistry — Organic Chemistry

Exploring some substituted chalcones as DprE1 inhibitors for antitubercular activity: An integrated computational approach <title>Abstract</title> The development of extensively drug resistance to Mycobacterium has further hindered WHO efforts in the fight against tuberculosis. Elucidation of new mechanistic pathways against drug-resistant TB is of primary concern. The current study aims to find potential chalcones via an in silico assessment against Decaprenylphosphoryl-β-D-ribose-2-oxidase (DprE1) which is a crucial target for ﬁnding antitubercular drug candidates. We prospected the in silico properties of a library of 30 chalcones having two aromatic rings and either an electron withdrawing or electron donating substituent against DprE1 enzyme. The results showed that the introduction of p-chloro or p-methyl substituents and an indole on the chalcones’ motif played critical roles in their inhibition of Mtb DprE1. These chalcones demonstrated favorable physicochemical, pharmacokinetic, and CYP metabolism characteristics, suggesting that they could be promising drug candidates for further investigation. Analysis of docked complexes revealed that non-covalent bonding interactions are important for the binding and stability of the chalcones within the active site of the protein with estimated free energy of binding ranging from -10.4 to -6.5 kcal/mol. The anti-TB sensitivity findings suggest that the compounds were more potent compared to the pyrazinamide against Mycobacterium tuberculosis. Furthermore, the binding stability study performed using molecular dynamics simulation of the DprE1-9C complex disclosed the conformational stability of the complex over 200 ns with average root mean square deviation not exceeding 0.25 nm (2.5 Å). The computational results could immensely contribute toward hit to lead triaging in the search for new DprE1 inhibitors with improved antitubercular activities. Computer science ; Combinatorial chemistry ; Chemistry ; Business ; Computational biology ; Biology Synthesis and biological activity
Authors: Qingrong Liang | Guozhong Zheng | Yang Liu | Shoujun Zheng
Source: arXiv (Cornell University) (preprint, 2024-07-31)
Field: Materials Science — Materials Chemistry

Multiple sliding ferroelectricity of rhombohedral-stacked InSe for reconfigurable photovoltaics and imaging applications Through stacking engineering of two-dimensional (2D) materials, a switchable interface polarization can be generated through interlayer sliding, so called sliding ferroelectricity, which is advantageous over the traditional ferroelectricity due to ultra-thin thickness, high switching speed and low fatigue. However, 2D materials with intrinsic sliding ferroelectricity are still rare, with the exception of rhombohedral-stacked MoS2, which limits sliding ferroelectricity for practical applications such as high-speed storage, photovoltaic, and neuromorphic computing. Here, we reported the observation of sliding ferroelectricity with multiple states in undoped rhombohedral-stacked InSe (γ-InSe) via dual-frequency resonance tracking piezoresponse force microscopy, scanning Kelvin probe microscopy and conductive atomic force microscopy. The tunable bulk photovoltaic effect via the electric field is achieved in the graphene/γ-InSe/graphene tunneling device with a photovoltaic current density of ~15 mA/cm2, which is attributed to the multiple sliding steps in γ-InSe according to our theoretical calculations. The vdw tunneling device also features a high photo responsivity of ~255 A/W and a fast response time for real-time imaging. Our work not only enriches rhombohedral-stacked 2D materials for sliding ferroelectricity, but also sheds light on their potential for tunable photovoltaics and imaging applications. Photovoltaics ; Ferroelectricity ; Trigonal crystal system ; Materials science ; Optoelectronics ; Nanotechnology ; Engineering ; Crystallography ; Photovoltaic system ; Electrical engineering ; Chemistry ; Crystal structure Solid-state spectroscopy and crystallography ; Chalcogenide Semiconductor Thin Films ; Semiconductor materials and interfaces
Authors: Kamyar Barakati | Yu Liu | Chris Nelson | Maxim Ziatdinov | Xiaohang Zhang | Ichiro Takeuchi | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2024-11-19)
Field: Materials Science — Surfaces, Coatings and Films

Reward driven workflows for unsupervised explainable analysis of phases and ferroic variants from atomically resolved imaging data Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, we explore the effects of descriptors and hyperparameters on the capability of unsupervised ML methods to distill local structural information, exemplified by discovery of polarization and lattice distortion in Sm doped BiFeO3 (BFO) thin films. We demonstrate that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards were designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows us to discover local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. We further extend the reward driven workflows to disentangle structural factors of variation via optimized variational autoencoder (VAE). Finally, the importance of well-defined rewards was explored as a quantifiable measure of success of the workflow. Workflow ; Computer science ; Computational biology ; Psychology ; Biology ; Database Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques