jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kathrin Korff | Johannes B. Mueller‐Reif | Dorothea Fichtl | Vincent Albrecht | Alicia-Sophie Schebesta | Ericka Corazon Mae Itang | Sabrina De Winter | Lesca M. Holdt | Daniel Teupser | Matthias Mann | Philipp E. Geyer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-10)
Field: Chemistry — Spectroscopy

Pre-Analytical Drivers of Bias in Bead-Enriched Plasma Proteomics Abstract Bead-based enrichment has emerged as a promising strategy to improve depth in plasma proteomics by overcoming the dynamic range barrier. However, its robustness against pre-analytical variation has not been sufficiently characterized. Here, we systematically evaluate five plasma proteomics workflows—including three bead-based methods and two conventional protocols—using controlled spike-ins of low-abundance proteins and defined cellular contaminants. We find that bead-based approaches enhance detection of low-abundance proteins but can be highly susceptible to systematic bias from platelet and PBMC contamination, even at low levels. This can easily inflate results by thousands of proteins, likely accounting for some of the very high literature-reported numbers. In contrast, a perchloric acid-based workflow shows notable resistance to erythrocyte and platelet-derived contamination. We further investigate how centrifugation conditions, anticoagulant choice, and buffer-bead combinations modulate contamination profiles and demonstrate that bias can partially be mitigated by optimized sample handling. In total, we identify more than 13,000 different protein groups from various conditions, including cellular components from the circulating proteome. Our results provide a quantitative framework for assessing workflow performance under variable sample quality and offer guidance for both biomarker discovery and quality control in clinical proteomics studies. Bead ; Plasma ; Chromatography ; Chemistry ; Materials science ; Physics ; Nuclear physics ; Composite material Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches
Authors: Mohamed A. N. Soliman | Abdulwahhab Khedr | Tarsem Sahota | Rachel Armitage | R N Allan | Katie Laird | Natalie Allcock | Fatmah I. Ghuloum | Mahetab H. Amer | Reem S. Alazragi | Charlotte J. C. Edwards‐Gayle | Jacek K. Wychowaniec | Attilio V. Vargiu | Mohamed A. Elsawy
Source: Small (article, 2025-01-09)
Field: Materials Science — Biomaterials

Unraveling the Atomistic Mechanism of Electrostatic Lateral Association of Peptide β‐Sheet Structures and Its Role in Nanofiber Growth and Hydrogelation Guiding molecular assembly of peptides into rationally engineered nanostructures remains a major hurdle against the development of functional peptide-based nanomaterials. Various non-covalent interactions come into play to drive the formation and stabilization of these assemblies, of which electrostatic interactions are key. Here, the atomistic mechanisms by which electrostatic interactions contribute toward controlling self-assembly and lateral association of ultrashort β-sheet forming peptides are deciphered. Our results show that this is governed by charge distribution and ionic complementarity, both affecting the interaction patterns between charged residues: terminal, core, and/or terminal-to-core attraction/repulsion. Controlling electrostatic interactions enabled fine-tuning nanofiber morphology for the 16 examined peptides, resulting into versatile nanostructures ranging from extended thin fibrils and thick bundles to twisted helical "braids" and short pseudocrystalline nanosheets. This in turn affected the physical appearance and viscoelasticity of the formed materials, varying from turbid colloidal dispersions and viscous solutions to soft and stiff self-supportive hydrogels, as revealed from oscillatory rheology. Atomistic mechanisms of electrostatic interaction patterns were confirmed by molecular dynamic simulations, validating molecular and nanoscopic characterization of the developed materials. In essence, detailed mechanisms of electrostatic interactions emphasizing the impact of charge distribution and ionic complementarity on self-assembly, nanostructure formation, and hydrogelation are reported. Nanofiber ; Nanostructure ; Electrostatics ; Ionic bonding ; Nanotechnology ; Materials science ; Chemical physics ; Molecular dynamics ; Self-assembly ; Nanoscopic scale ; Static electricity ; Self-healing hydrogels ; Complementarity (molecular biology) ; Electrostatic interaction ; Rheology ; Covalent bond ; Chemistry ; Computational chemistry ; Ion ; Polymer chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Luminescence and Fluorescent Materials
Authors: Andreas Kartakoullis | Nicola Caporaso | Martin B. Whitworth | Ian D. Fisk
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-02-07)
Field: Chemistry — Analytical Chemistry

Gaussian mixture model clustering allows accurate semantic image segmentation of wheat kernels from near-infrared hyperspectral images In this study, an ad-hoc image processing pipeline has been developed and proposed for the purpose of semantically segmenting wheat kernel data acquired through near-infrared hyperspectral imaging (HSI). The Gaussian Mixture Model (GMM), characterized as a soft clustering method, has been employed for this task, yielding noteworthy results in both kernel and germ segmentation. A comparative analysis was conducted, wherein GMM was compared with two hard clustering methods, hierarchical clustering and k-means, as well as other common clustering algorithms prevalent in food HSI applications. Notably, GMM exhibited the highest accuracy, with a Jaccard index of 0.745, surpassing hierarchical clustering at 0.698 and k-means at 0.652. Furthermore, the spectral variations observed in wheat kernel topology can be used for semantic image segmentation, especially in the context of selecting the germ portion within the wheat kernels. These findings carry practical significance for professionals in the fields of hyperspectral imaging (HSI) and machine vision, particularly for food product quality assessment and real-time inspection. • Hyperspectral imaging high-throughput real-time analysis requires segmentation. • Semantic image segmentation with Gaussian mixture model clustering was applied. • The proposed method allows accurate classification of the embryo region. • Soft clustering algorithms can outperform hard clustering algorithms. • Wheat kernel structural information can be studied through NIR spectral variation. Hyperspectral imaging ; Mixture model ; Pattern recognition (psychology) ; Artificial intelligence ; Cluster analysis ; Computer science ; Segmentation ; Gaussian network model ; Image (mathematics) ; Infrared ; Gaussian ; Computer vision ; Chemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Advanced Chemical Sensor Technologies
Authors: Oliver Kardell | Thomas Gronauer | Christine von Toerne | Juliane Merl‐Pham | Ann‐Christine König | Teresa K. Barth | Julia Mergner | Christina Ludwig | Johanna Tüshaus | Pieter Giesbertz | Stephan Breimann | Lisa Schweizer | Torsten Müller | Georg Kliewer | Ute Distler | David Gómez-Zepeda | Oliver Popp | Di Qin | Daniel Teupser | Jüergen Cox | Axel Imhof | Bernhard Küster | Stefan F. Lichtenthaler | Jeroen Krijgsveld | Stefan Tenzer | Philipp Mertins | Fabian Coscia | Stefanie M. Hauck
Source: Journal of Proteome Research (article, 2025-02-07)
Field: Chemistry — Spectroscopy

Multicenter Longitudinal Quality Assessment of MS-Based Proteomics in Plasma and Serum Advancing MS-based proteomics toward clinical applications evolves around developing standardized start-to-finish and fit-for-purpose workflows for clinical specimens. Steps along the method design involve the determination and optimization of several bioanalytical parameters such as selectivity, sensitivity, accuracy, and precision. In a joint effort, eight proteomics laboratories belonging to the MSCoreSys initiative including the CLINSPECT-M, MSTARS, DIASyM, and SMART-CARE consortia performed a longitudinal round-robin study to assess the analysis performance of plasma and serum as clinically relevant samples. A variety of LC-MS/MS setups including mass spectrometer models from ThermoFisher and Bruker as well as LC systems from ThermoFisher, Evosep, and Waters Corporation were used in this study. As key performance indicators, sensitivity, precision, and reproducibility were monitored over time. Protein identifications range between 300 and 400 IDs across different state-of-the-art MS instruments, with timsTOF Pro, Orbitrap Exploris 480, and Q Exactive HF-X being among the top performers. Overall, 71 proteins are reproducibly detectable in all setups in both serum and plasma samples, and 22 of these proteins are FDA-approved biomarkers, which are reproducibly quantified (CV < 20% with label-free quantification). In total, the round-robin study highlights a promising baseline for bringing MS-based measurements of serum and plasma samples closer to clinical utility. Orbitrap ; Bioanalysis ; Proteomics ; Quantitative proteomics ; Workflow ; Mass spectrometry ; Reproducibility ; Blood proteins ; Chromatography ; Computer science ; Chemistry ; Medicine ; Internal medicine ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: Rishabh Sharma | Smarajit Karmakar
Source: Nature Physics (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Activity-induced annealing leads to a ductile-to-brittle transition in amorphous solids Active glasses are dense and disordered systems consisting of motile particles that display phenomenology observed in many biological systems. Here we investigate motility-driven annealing and fluidization in these systems and establish a correspondence between the yielding behaviour of glassy systems under active dynamics and their yielding under oscillatory shear. The yielded region of the phase diagram correlates with tissue fluidization, whereas the annealing region explains age-related maturation and stiffening. This suggests that some mechanical changes observed in ageing tissues can partially stem from processes analogous to enhanced ageing observed in active glasses. In addition to showing similar yielding diagrams, we strengthen the correspondence to oscillatory shear by demonstrating diverging time scales to steady states, the possibility of memory encoding and reading, and the importance of stress reversals in the annealing process in both cases. Finally, we study yielding in active solids and demonstrate that given the correct geometry, one can either suppress or promote brittle failure via shear band formation by tuning activity. Active glasses are dense, disordered structures made up of motile constituents. Simulations now show that motility-driven annealing in such systems leads to mechanical changes, including increased brittleness. Physics ; Amorphous solid ; Annealing (glass) ; Brittleness ; Condensed matter physics ; Crystallography ; Thermodynamics ; Chemistry Material Dynamics and Properties ; Building materials and conservation ; High-pressure geophysics and materials
Authors: Yanwu Long | Chengquan Zhong | Xiaojing Ma | Jingzi Zhang | Honghao Yao | Jiakai Liu | Kailong Hu | Qian Zhang | Xi Lin
Source: ACS Applied Materials & Interfaces (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Inverse Design of High-Performance Thermoelectric Materials via a Generative Model Combined with Experimental Verification The emergence of inverse design approaches leveraging generative models offers a promising avenue for thermoelectric material design. However, these models heavily depend on diverse training data, and current thermoelectric data sets are limited, primarily encompassing group IV-VI materials operating within moderate temperature ranges. This constraint poses a significant challenge in the pursuit of materials with high thermoelectric figure of merit (<i>zT</i>) through generative modeling. Our study introduces an inverse design model tailored for the constrained thermoelectric materials data set. By augmenting the data with 2000 entries from the experimental literature and incorporating a generative model featuring a diversity loss function and residual network (ResNet) architecture to enhance complexity, our approach has been trained to systematically generate high-<i>zT</i> thermoelectric materials across various temperature ranges. Under predefined high-<i>zT</i> criteria, our deep generative model successfully predicted 100 doped materials with <i>zT</i> values exceeding 1.0. Furthermore, this research analyzes density of states (DOS) plots for the generated materials, identifying 25 unreported previously potential thermoelectric candidates in the material database. Notably, we experimentally validated the synthesis of Mg<sub>3.1</sub>Sb<sub>0.5</sub>Bi<sub>1.497</sub>Te<sub>0.003</sub>, a representative thermoelectric material from the Mg<sub>3</sub>(Sb, Bi)<sub>2</sub> family suitable for room temperature applications. This validation underscores the efficacy of our model in exploring and discovering novel thermoelectric materials. Materials science ; Inverse ; Thermoelectric materials ; Material Design ; Nanotechnology ; Process engineering ; Composite material ; Thermal conductivity Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Muhammad Shafiq | Kalpana Thakre | Raji Pandurangan | R. V. S. Lalitha
Source: The International Journal of Advanced Manufacturing Technology (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Generative AI designs the next generation of smart materials from pixels to products Generative grammar ; Pixel ; Artificial intelligence ; Computer science ; Engineering drawing ; Engineering ; Computer vision ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Lucas P. Kreuzer | Marie Betker | Marcell Wolf | Bart‐Jan Niebuur | Jacques Ollivier | Daniel Söderberg | Stephan V. Roth
Source: Macromolecules (article, 2025-02-18)
Field: Materials Science — Polymers and Plastics

Impact of Humidity on Water Dynamics and Electrical Conductivity in PEDOT:PSS/Cellulose Nanofibril Nanocomposite Films: Insights from Quasi-Elastic Neutron Scattering The water dynamics in a nanocomposite film that consists of the electrically conductive poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and cellulose nanofibrils (CNFs) have been investigated during three cycles of exposure to low and high relative humidity (RH = 5% and 85%, respectively) using quasi-elastic neutron scattering (QENS). The obtained dynamical structure factors are transformed into the imaginary part of the dynamic susceptibility to better differentiate between the individual relaxation processes. In a humid environment, two different water species are present inside the films: fast-moving bulk water and slow-moving hydration water. During the first cycle, a large amount of hydration water enhances the polymer chain mobility, eventually leading to irreversible structural rearrangements within the film. In the subsequent cycles, we observed a release of all bulk water and portions of hydration water upon drying, along with an uptake of both water species in a humid environment. The relaxation times of hydration water diffusion as a function of momentum transfer can be described by a jump-diffusion model. The obtained jump lengths, residence times, and diffusion coefficients of hydration water suggest a change in the hydration layer upon drying: water molecules around hydrophobic groups are released from the film, while the hydrogen bonds between water and hydrophilic groups are sufficiently strong to keep these molecules inside the films, even in a dry state. The QENS results can be correlated to the structural and conductive properties. In the dry state, the low hydration water content and the absence of bulk water allow for improved wetting of the CNFs by PEDOT:PSS, which eventually increases the electrical conductivity of the films. PEDOT:PSS ; Neutron scattering ; Nanocomposite ; Materials science ; Cellulose ; Conductivity ; Humidity ; Chemical engineering ; Scattering ; Electrical resistivity and conductivity ; Small-angle neutron scattering ; Polymer chemistry ; Composite material ; Polymer ; Chemistry ; Physical chemistry ; Optics ; Thermodynamics ; Physics Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Safaa R. Fouda | Asmaa Abuessawy | Adel A.‐H. Abdel‐Rahman | Hagar S. El-Hema | M.A.R. Eisa | Mohamed A. Hawata
Source: Applied Water Science (article, 2025-01-25)
Field: Chemistry — Organic Chemistry

New functionalized magnetite chitosan–heterocyclic nanocomposites excelling in Cd2+ removal from aqueous solution with biological activity Abstract Newly modified magnetic chitosan nanoparticles were synthesized for the high-efficiency removal of cadmium ions from aquatic environments. The structure and morphology of the composites were confirmed using FTIR, TGA, XRD, BET, SEM, TEM, SAED pattern analysis, and zeta potential. Under optimal conditions (pH = 6.0, contact time = 120 min, and sorbent dosage = 0.005 g/100 mL), the maximum removal of Cd (II) by synthesized samples MC, SA, and SB was 90.8%, 95%, and 95.15%, respectively. The kinetic data at optimal pH = 6 could be accurately described using a pseudo-second-order equation, while the Langmuir isotherm model best represented the sorption process. The Langmuir Q max values for the synthesized nanocomposites S*, S3, S4, S*t, S3t, and S4t were 243.90, 250, 270.27, 212.76, 237.09, and 238.09, respectively. The thermodynamic analysis of these samples shows that the sorption process was exothermic (negative ∆H°) − 15.148, − 13.60, − 13.18, − 18.163, − 16.820, and − 16.06 kJmol −1 , respectively. This implies that the diffusion of Cd(II) on the sorbent decreased with the temperature rise. The positive values of ΔS⁰ were 152.37, 147.80, 146.83, 160.94, 157.08, and 154.93 kJmol −1 , respectively. The antimicrobial properties of the functionalized sorbents SA and SB were evaluated by measuring the zone of inhibition (ZOI) against Staphylococcus aureus and Klebsiella pneumoniae, representing Gram-negative and Gram-positive bacteria, respectively. SA and SB showed promising activity, demonstrating clear zones of (20 ± 0.2 and 18 ± 0.4 mm), and (22 ± 0.2 and 20 ± 0.4 mm) for S. aureus and K. pneumoniae, respectively. Additionally, these sorbents demonstrated moderate antioxidant activity, with the highest scavenging percentages of 94.20% and 96.63%, respectively, at a concentration of 1000 µg/mL, as assessed by the DPPH method. Chitosan ; Magnetite ; Aqueous solution ; Nanocomposite ; Industrial and production engineering ; Magnetite Nanoparticles ; Chemical engineering ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Nanoparticle ; Engineering ; Magnetic nanoparticles ; Metallurgy Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery ; Extraction and Separation Processes
Authors: Ahmed E. Alprol | Ahmed M. Bakr | Sameerah I. Al‐Saeedi | Abdallah Tageldein Mansour | Ehab El‐Haroun | Maymounah N. Alharthi | Mohamed Ashour
Source: Scientific Reports (article, 2025-07-02)
Field: Chemistry — Organic Chemistry

Chitosan/ferrous oxide nanocomposite for the sunlight-driven photocatalytic degradation of organic azo dye in aqueous solutions and aquaculture effluents wastewater The current work presents a novel and sustainable approach that uses the synthesized Chitosan/Ferrous Oxide Nanocomposite (CFON) to remove the organic azo dye, Acid Red 73 (AR73), a hazardous pollutant commonly found in industrial wastewater, from aqueous solutions and aquaculture effluents wastewater. The CFON exhibited excellent photocatalytic degradation performance under sunlight irradiation. Batch adsorption experiments were conducted to investigate the influence of various parameters on the removal efficiency of the dye, including pH, contact time, adsorbent dosage, initial concentration, and temperature. The results showed that the nanocomposite effectively achieved a maximum removal percentage of 99.3% under optimal conditions (pH 5 for 180 min at 80 and 100 mg with 75 mg L<sup>-1</sup> and 30 °C). Kinetic studies revealed that the adsorption process followed a pseudo-second-order model, indicating that chemisorption is the dominant mechanism. The impact of different light sources on dye removal efficiency was evaluated, confirming the superior performance of sunlight-driven photocatalysis. The application of CFON to improve water quality in aquaculture was investigated and showed effectively reduced conductivity, total dissolved solids, turbidity, and nitrate levels, demonstrating its potential for sustainable water management. This work highlights the promising potential of CFON as an environmentally friendly and efficient material to remove AR73 and improve aquaculture wastewater quality. Chitosan ; Nanocomposite ; Wastewater ; Aqueous solution ; Ferrous ; Effluent ; Photocatalysis ; Degradation (telecommunications) ; Chemistry ; Pulp and paper industry ; Photodegradation ; Nuclear chemistry ; Materials science ; Environmental science ; Organic chemistry ; Nanotechnology ; Environmental engineering ; Catalysis ; Computer science Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Olivier Henrotte | Seryio Saris | Franz Gröbmeyer | Christoph Gruber | Ismail Bilgin | Alexander Högele | Naomi J. Halas | Peter Nordlander | Emiliano Cortés | Alberto Naldoni
Source: Nature Communications (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Spatially resolved photocatalytic active sites and quantum efficiency in a 2D semiconductor Identifying reactive sites and measuring their activities is crucial for enhancing the efficiency of every catalyst. Reactivity maps can guide the development of next-generation photocatalysts like 2D transition metal dichalcogenides, which suffer from low conversion rates. While their electrocatalytic sites are well-studied, their photocatalytic sites remain poorly understood. Using scanning photoelectrochemical microscopy, we spatially resolve the photoreactivity of MoS<sub>2</sub> monolayers, a prototypical 2D transition metal dichalcogenide, for redox reactions, including H<sub>2</sub> production from water. Aligned-unaligned excitation-detection measurements reveal that photogenerated holes and electrons exhibit distinct behaviors. Oxidation products localize at the excitation spot, indicating stationary holes, while photoreduction occurs up to at least 80 microns away, showing exceptional electron mobility. We also elucidate the photochemical reactivity according to the nature of the electronic excitation, showing that the internal quantum efficiency of strongly-bound A-excitons outperforms weakly-bound (free-carrier like) C-excitons across the flake. These findings offer novel guidance to rationally design 2D photocatalysts via engineering their optical and charge extraction abilities for efficient solar energy conversion. Photocatalysis ; Reactivity (psychology) ; Exciton ; Semiconductor ; Photochemistry ; Excitation ; Materials science ; Redox ; Quantum efficiency ; Catalysis ; Transition metal ; Monolayer ; Chemical physics ; Charge carrier ; Optoelectronics ; Nanotechnology ; Chemistry ; Physics ; Condensed matter physics 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Advanced biosensing and bioanalysis techniques