jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhitong Jiang | Zachariah Vicars | Suruchi Fialoke | Sumanth N. Jamadagni | Peter H. Koenig | Amish J. Patel
Source: ChemRxiv (preprint, 2025-01-08)
Field: Materials Science — Materials Chemistry

Thermodynamics of Self Assembly and Supramolecular Transitions using Enhanced Sampling Computational studies of self-assembly have the potential to provide rich insights into their underlying thermodynamics and identify optimal system conditions for applications, such as nanomaterial synthesis or drug delivery. However, both self-assembly and supramolecular transitions can be hindered by free energy barriers, rendering them rare events on molecular timescales and making it challenging to sample them. Here, we show that the use of enhanced sampling techniques, when combined with a judiciously chosen set of order parameters, offers an efficient and robust route for characterizing the thermodynamics of self-assembly and supramolecular transitions. Specifically, we show that transitions between states with different periodicities or symmetries can be reversibly sampled by biasing a relatively small number of Fourier components of the particle density. We illustrate our approach by computing the free energy required to cleave a liquid slab and estimating the corresponding liquid-vapor surface tension. We also characterize the free energetics of the transition between spherical and rod-shaped droplets. These results serve as a first step towards the development of a systematic computational framework for exploring transitions in diverse supramolecular systems, such as surfactants or block copolymers, and characterizing the thermodynamics of their self-assembly. Supramolecular chemistry ; Thermodynamics ; Sampling (signal processing) ; Materials science ; Statistical physics ; Chemistry ; Physics ; Crystallography ; Crystal structure Material Dynamics and Properties ; Advanced Thermodynamics and Statistical Mechanics ; nanoparticles nucleation surface interactions
Authors: Randall Q. Snurr | Daniel M. Abrams | Todd R. Gingrich | M. Grayson | Niall M. Mangan
Source: Diffusion fundamentals. (article, 2025-11-13)
Field: Chemistry — Physical and Theoretical Chemistry

Foreword: Special issue “Diffusion Fundamentals XI on the campus of Northwestern University in Evanson, Work (physics) ; Government (linguistics) ; Subject (documents) ; Exposition (narrative) Advanced Physical and Chemical Molecular Interactions ; Theoretical and Computational Physics ; Advanced Thermodynamics and Statistical Mechanics
Authors: Kinshuk Dasgupta
Source: Journal of ISAS (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Raman Spectroscopy in the Research of Carbon Materials Raman spectroscopy has become an indispensable tool in the field of material science, especially in the study of carbon materials. Its non-destructive nature, sensitivity to molecular vibrations, and ability to provide detailed information about the chemical composition, structure, and crystallinity make it a powerful technique for characterizing a wide range of carbon-based materials. This article delves into the significance of Raman spectroscopy in the research of carbon materials, exploring its principles, the types of carbon materials it can analyze, and the key findings it has facilitated Raman spectroscopy ; Materials science ; Carbon fibers ; Spectroscopy ; Analytical Chemistry (journal) ; Nanotechnology ; Chemistry ; Physics ; Optics ; Environmental chemistry ; Composite material ; Astronomy Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Boron and Carbon Nanomaterials Research
Authors: Marek Kucbel | Helena Raclavská | Jana Růžičková | Michal Šafář | Pavel Kantor | Karolína Slamová | Jarmila Drozdová
Source: Environmental Science and Pollution Research (article, 2025-11-07)
Field: Materials Science — Biomaterials

Removal of fillers and chemical reagents from waste paper for its sustainable use Waste paper represents a valuable secondary source of cellulose fibres, contributing to the reduction of virgin wood consumption in paper production. To support sustainable development objectives, the integration of natural fibres with synthetic polymers is increasingly explored across industrial sectors. Reliable use of waste paper materials requires baseline data on pollutant content and migration potential. In this study, cellulose fibres were separated using 0.2 M acetic acid (CH<sub>3</sub>COOH) extraction, followed by washing. This method effectively removed precipitated calcium carbonate-based filler from office paper, achieving an extraction efficiency of 86%. A total of 138 compounds were identified in waste paper, originating from: virgin wood (n = 31), paper manufacturing and recycling processes (n = 19 + 15 fragrance compounds), and printing inks (n = 67), with solvents (n = 25) forming the largest subgroup. Additional substances were associated with surface treatments and ink formulations. Compound hazard profiles were assessed using the Globally Harmonized System (GHS). The identified substances were, among others, persistent organic pollutants, including benzophenone, butylated hydroxytoluene, and bis(2-ethylhexyl) phthalate, bisphenol A, and bisphenol S, while solvents exhibited the highest proportion of hazardous classifications. Following CH<sub>3</sub>COOH extraction, concentrations of hazardous solvents were reduced by 93%, indicating the method's potential for pollutant mitigation in fibre recovery. Hazardous waste ; Cellulose ; Bisphenol A ; Raw material ; Pollutant ; Reagent ; Bisphenol ; Extraction (chemistry) Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Natural Fiber Reinforced Composites
Authors: Bohdan Trembach | Oleh Dmitriiev | Kostiantyn Kulahin | Oleksii Balenko | Volodymyr Maliuha | Larysa Neduzha
Source: Preprints.org (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

Hybrid Optimization of Hardfacing Conditions and the Content of Exothermic Additions in the Core Filler During the Flux-Cored Arc Welding Process This study introduces a hybrid optimization method to enhance the melting characteristics and weld bead morphology of flux-cored wire hardfacing with exothermic addition into the core filler. The Taguchi Design of Experiments (L9 orthogonal array) was used to analyze the effects of key conditions on multiple melting characteristics. The hybrid Taguchi-GRA-PCA effectively identified the best parameter combination, resulting in a significant improvement in overall melting performance. The impact of welding modes on weld bead parameters and melting characteristics was examined. It was determined that the optimal amount of the exothermic addition CuOAl introduced into the flux-cored wire filler should be at a medium level (EA = 28 wt.%). Results showed that wire feed speed WFS and EA had the greatest effect on MOR and DR, while EA and CTWD mainly influenced SF and De. It has been determined that the content of the exothermic additive has a significant impact on the melting process of filler materials, affecting the melting characteristics and weld bead morphology. It has been found that the melting characteristics of deposition rate and spattering factor can be used to optimize welding modes and characterize most output parameters of the welding/surfacing process. Exothermic reaction ; Hardfacing ; Welding ; Taguchi methods ; Core (optical fiber) ; Orthogonal array ; Filler (materials) ; Gas metal arc welding Metal Alloys Wear and Properties ; Welding Techniques and Residual Stresses ; Engineering and Environmental Studies
Authors: Alina Elena Coman | Ana Maria Roșca | Maria Minodora Marin | Mădălina Georgiana Albu Kaya | Augusta Raluca Gabor | Cătălina Diana Uşurelu | Mihaela Violeta Ghica | Laurențiu Dinca | Irina Titorencu
Source: Pharmaceutics (article, 2025-12-26)
Field: Materials Science — Biomaterials

Development and Evaluation of Scaffolds Based on Perch Collagen–Hydroxyapatite for Advanced Synthetic Bone Substitutes <b>Background/Objectives:</b> Bone defects remain widespread. Type I collagen-hydroxyapatite composites suit bone engineering by mimicking matrix structure, making them pertinent materials for bone tissue engineering across a range of defect types. Their application is well aligned with non-load-bearing conditions, while use in load-bearing sites requires mechanical properties that meet the demands of those environments. Marine collagen offers a low-cost source from processing by-products. This work aimed to develop perch collagen-hydroxyapatite scaffolds for bone tissue engineering. <b>Methods:</b> Composites with COLL:HAp ratios of 100:0, 50:50, 40:60, and 30:70 were prepared. After crosslinking and freeze-drying, porosity and water absorption were examined. SEM and X-EDS assessed morphology and elemental distribution. FT-IR confirmed the chemical composition. Compression tests evaluated mechanical behavior. Cell viability and colonization assessed the biological performance. Biodegradability, thermal stability, and antimicrobial activity were also determined. <b>Results:</b> FT-IR confirmed the characteristic absorption bands of both components. SEM and swelling behavior showed porous, interconnected structures with uniform hydroxyapatite dispersion. X-EDS indicated Ca/P ratios consistent with hydroxyapatite. Thermal analysis demonstrated scaffold stability. Compression tests showed mechanical resistance for all the scaffolds, with stiffness increasing with the inorganic content. Perch collagen enhanced biological functionality, supporting osteoblast viability and colonization. Biodegradation gradually proceeded. Antibacterial activity against the tested pathogens was detectable, though moderate. <b>Conclusions:</b> The developed scaffolds combined structural stability, controlled degradation, and favorable cell response, constituting a viable and promising candidate for applications in bone tissue engineering. Scaffold ; Viability assay ; Tissue engineering ; Osteoblast ; Bone tissue ; Absorption of water ; Artificial bone ; Matrix (chemical analysis) Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Periodontal Regeneration and Treatments
Authors: Prakash K. Dubey | Jyoti Bhadauria
Source: International Journal on Science and Technology (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Lattice Dynamical Studies of Transition Metal Carbides Transition metal carbides (TMCs) have gained significant attention due to their exceptional combination of metallic and ceramic properties, including high hardness, thermal conductivity, and chemical stability. These materials, such as TiC, ZrC, HfC, VC, and NbC, are widely used in extreme environments like aerospace and high-temperature industries. Lattice dynamical studies provide crucial insights into their atomic vibrations, bonding nature, and thermodynamic stability. This paper focuses on the analysis of phonon behavior, elastic properties, and thermodynamic parameters of transition metal carbides using both theoretical and experimental methods. Computational approaches based on density functional theory (DFT) and experimental techniques like neutron scattering are discussed to interpret phonon dispersion and vibrational spectra. The results reveal a strong link between lattice vibrations and material properties, showing how phonons influence mechanical strength, thermal stability, and even superconducting behavior in certain carbides. These findings contribute to a deeper understanding of lattice dynamics and open new pathways for designing advanced high-performance materials. Phonon ; Carbide ; Lattice (music) ; Superconductivity ; Ceramic ; Thermal ; Neutron scattering ; Transition metal Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; Metallurgical and Alloy Processes
Authors: Zachary Martin | Sharon M. Weiss
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Multichromatic porous silicon RGB rugate filters for use in smartphone biosensing In this work, we report a porous silicon-based multichromatic rugate filter to select red (R), green (G), and blue (B) wavelengths from white light illumination provided by a smartphone. The full-width-at-half-maximum values for all three color bands are below 30 nm. Spectral characterization was carried out with a spectrometer and chromatic analysis was applied to smartphone images of the filter to verify desired performance. Conversion of white light from the LED flash of a smartphone to multichromatic RGB illumination can be used in smartphone-based biosensing for improved limit of detection. RGB color model ; Filter (signal processing) ; Flash (photography) ; Color gel ; Optical filter ; Porous silicon ; Wavelength ; Chromatic scale ; Reflectivity Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Thin-Film Transistor Technologies
Authors: Pengkang Chang | Shuaishuai Ding | Bing‐Bing Liang | Yuhua Cao | Gan Tian | Ji Zheng
Source:  (review, 2025-02-28)
Field: Materials Science — Materials Chemistry

Global Research Trends in Nanotechnology and Materials for Prostate Cancer Diagnosis and Treatment: A Review and Visual Analysis not-yet-known not-yet-known not-yet-known unknown Prostate cancer is the most frequently diagnosed cancer and the deadliest malignancy of the male urinary system. Patients’ clinical progression and outcomes vary widely; however, in recent years, major advancements in both diagnosis and treatment have occurred, particularly with the application of nanotechnology in this field. Despite the substantial number of original articles and reviews published annually on prostate cancer diagnosis and treatment, no bibliometric analysis has been conducted on this topic. This study aimed to identify key themes, research hotspots, and knowledge gaps regarding the use of nanotechnology in prostate cancer diagnosis and treatment through bibliometric analysis and visualization of the literature. It also involved summarizing recent advancements to explore new research directions. We analyzed the quantity and quality of publications on nanotechnology for prostate cancer diagnosis and treatment using the Web of Science core collection database. We then examined publishing trends by country, institution, and author, and assessed cooperation networks. Subsequently, we categorized and summarized research “hot topics,” and summarized the latest findings regarding nanotechnology for prostate cancer diagnosis and treatment. Prostate cancer ; Cancer ; Nanotechnology ; Data science ; Medicine ; Medical physics ; Computer science ; Materials science ; Internal medicine Machine Learning in Materials Science
Authors: Namgil Lee | Hojin Yoo | Dohyun Han | Heejung Yang
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-12)
Field: Chemistry — Spectroscopy

Regularized Deep Neural Networks for Combining Heterogeneous Features of Peptides in Data Independent Acquisition Mass Spectrometry Abstract Data-independent acquisition (DIA) has gained much attention in mass spectrometry (MS)-based proteomics for its improved reproducibility and unbiased data acquisition. In DIA-MS, the spectral library is crucial in peptide identification. However, this method is limited to peptides previously identified via data-dependent acquisition (DDA) MS experiments. This study proposes a deep learning approach for generating spectral libraries, even for previously unseen peptides. While most deep learning-based methods rely on one-hot encoding representation for peptides, the proposed method incorporates physicochemical features, including atomic composition, hydrophobicity, flexibility, fractional surface probability, and aromaticity. We introduce sparsity regu-larized neural network layers to facilitate the selection and combination of important high-dimensional physicochemical features and improve prediction performance. Fur-thermore, we suggest a transfer learning strategy for training the proposed deep neural networks having multiple heterogeneous input channels. Numerical experiments using benchmark DDA-MS data demonstrated that the proposed deep learning model out-performed existing benchmark models, such as Prosit and DeepDIA, particularly in predicting retention times. And it was demonstrated that the proposed models with sparsity regularization identified more peptides from HeLa cell DIA data compared to the other deep learning models. Mass spectrometry ; Artificial neural network ; Computer science ; Deep neural networks ; Artificial intelligence ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Vivian Torres Vera | Lorena Coronado | Ana Patricia Valencia | Alejandro Von Chong | Esteban Rua | Michelle G. Ng | Jorge Rubio‐Retama | Carmenza Spadafora | Ricardo Correa
Source: Biosensors (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Enhanced Visualization of Erythrocytes Through Photoluminescence Using NaYbF4:Yb3+,Er3+ Nanoparticles Rare-earth nanoparticles (RE-NPs), particularly NaYF<sub>4</sub>:Yb<sup>3+</sup>,Er<sup>3+</sup>, have emerged as a promising class of photoluminescent probes for bioimaging and sensing applications. These nanomaterials are characterized by their ability to absorb low-energy photons and emit higher-energy photons through an upconversion luminescence process. This process can be triggered by continuous-wave (CW) light excitation, providing a unique optical feature that is not exhibited by native biomolecules. However, the application of upconversion nanoparticles (UCNPs) in bioimaging requires systematic optimization to maximize the signal and ensure biological compatibility. In this work, we synthesized hexagonal-phase UCNPs (average diameter: 29 ± 3 nm) coated with polyacrylic acid (PAA) and established the optimal conditions for imaging human erythrocytes. The best results were obtained after a 4-h incubation in 100 mM HEPES buffer, using a nanoparticle concentration of 0.01 mg/mL and a laser current intensity of 250-300 mA. Under these conditions, the UCNPs exhibited minimal cytotoxicity and were found to predominantly localize at the erythrocyte membrane periphery, indicating surface adsorption rather than internalization. Additionally, a machine learning model (Random Forest) was implemented that classified the photoluminescent signal with 80% accuracy and 83% precision, with the signal intensity identified as the most relevant feature. This study establishes a quantitative and validated protocol that balances signal strength with cell integrity, enabling robust and automated image analysis. Photoluminescence ; Nanoparticle ; Visualization ; Materials science ; Optoelectronics ; Nanotechnology ; Computer science Luminescence Properties of Advanced Materials ; Nanoplatforms for cancer theranostics ; Luminescence and Fluorescent Materials
Authors: Anuj Debnath | Susanta Ghanta | Chandan K. Das
Source: Research Square (preprint, 2025-10-13)
Field: Materials Science — Materials Chemistry

Computational Free Energy Analysis Uncovers Anomalous Phase Transformation in Silicon Silicon ; Quenching (fluorescence) ; Melting point ; Melting curve analysis ; Diamond ; Molecular dynamics ; Phase (matter) ; Tetrahedron ; Drop (telecommunication) Material Dynamics and Properties ; Theoretical and Computational Physics ; High-pressure geophysics and materials
Authors: Ely G. F. de Miranda | Rafael S. Mattos | Saikat Mukherjee | Josene M. Toldo | Cheol Ho Choi | Márcio T. do N. Varella | Mario Barbatti
Source: ChemRxiv (article, 2025-08-25)
Field: Materials Science — Surfaces, Coatings and Films

Surface Hopping with Fully Correlated Methods Surface hopping simulations critically depend on the accuracy and robustness of the underlying electronic structure methods. Fully correlated approaches—such as CASPT2, MRCI, L-PDFT, and MRSF-TDDFT—that account for both dynamic and static electron correlation offer significant promise. Still, their practical application in dynamics remains limited by computational cost and technical challenges. In this perspective, we examine the current status of such methods by analyzing representative surface hopping simulations of fulvene and pyrrole, two prototypical systems for photophysical and photochemical processes. These examples demonstrate that while fully correlated methods improve the description of bond rearrangements and hot ground-state dynamics, partially correlated approaches—such as ADC(2) and TDDFT—remain sufficient for photophysical excited-state relaxation. Across methods, persistent limitations, such as active space instabilities and potential energy discontinuities, imply the need for improved approaches. We argue that expanding the use of generalized active spaces in the short term and advancing large active space algorithms in the long term will be crucial for making high-accuracy nonadiabatic simulations broadly reliable and accessible. Surface (topology) ; Mathematics ; Geometry Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Nanofabrication and Lithography Techniques
Authors: Neng Yan | Alessandro Parodi | Fei Dang | Jianbo Shi
Source: New Contaminants (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Shedding light on the invisible: aggregation-induced emission-based bioimaging for assessing the environmental impact of emerging nanoscale pollutants The rapid proliferation of emerging nanoscale contaminants (ENCs) poses significant challenges for traditional toxicological assessment due to their unique physicochemical properties and complex interactions with biological systems. This perspective highlights the transformative role of advanced bioimaging technologies in addressing these challenges by enabling direct visualization and quantification of ENCs within living systems. The strengths and limitations of current imaging techniques are explored, ranging from electron microscopy for ultra-high resolution to non-invasive whole-body imaging modalities. Particular emphasis is placed on the advantages of aggregation-induced emission luminogens (AIEgens), which overcome critical limitations of conventional fluorophores by offering exceptional photostability and biocompatibility. AIE-based bioimaging techniques enable real-time, dynamic tracking of ENCs and facilitate multiscale imaging, from elucidating subcellular uptake mechanisms to organelle interactions. Looking ahead, prevailing technical challenges are discussed, including the trade-offs between resolution and penetration depth, along with the need for robust quantitative analysis. The integration of smarter probes like AIEgens, multimodal imaging platforms, and artificial intelligence-powered analytics is expected to drive a paradigm shift from descriptive observation to predictive toxicology. This shift will make it possible to visualize the entire journey of ENCs in biological systems, link spatial and temporal behavior to molecular responses, and develop computational models for forecasting toxicity. Such advancements are essential for evidence-based risk assessment and the safe-by-design innovation of nanomaterials, ensuring the sustainable development of nanotechnology while protecting environmental health. Transformative learning ; Visualization ; Emerging technologies ; Analytics ; Scale (ratio) ; Temporal scales ; Human health Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Carbon and Quantum Dots Applications