jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kuldeep Vinchurkar | Sarad Pawar Naik Bukke | Priyansh Jain | Juhi Bhadoria | Manoj Likhariya | Sheetal Mane | Meghraj Suryawanshi | Kasturi Viswanathasetty Veerabhadrappa | Zohre Eftekhari | Hope Onohuean
Source: Discover Polymers. (article, 2025-02-12)
Field: Materials Science — Biomaterials

Advances in sustainable biomaterials: characterizations, and applications in medicine Biomaterials are essential in modern medicine, driving advancements in tissue engineering (TE), drug delivery, and medical devices. This review explores the intersection of biomaterials and sustainability, highlighting efforts to develop environmentally friendly materials through advanced technology. It examines key biomaterial characteristics for example biodegradability, mechanical strength, and biocompatibility, and discusses the influence of polymers on medical technology. From inert substances to bioactive materials, biomaterials have evolved to elicit biological responses. Notably, 3D-printed biomaterials are revolutionizing implant development with their customization and tissue facilitation advantages. The emergence of production-on-demand (POD) in 3D printing promises environmental sustainability and cost efficiency. Forecasts indicate significant growth in the global biomaterials market, particularly in Asia, reflecting increasing demand for biomaterial-based medical products. With applications ranging from synthetic skin to dental implants, biomaterials showcase versatility and importance in healthcare. Advanced characterization techniques and bioprocessing technologies enable the production of sustainable biomaterials, addressing environmental concerns. Despite challenges for example scalability and regulatory hurdles, ongoing research and collaboration are vital. Future efforts should prioritize bioprocessing optimization, novel biomaterial formulations, and life cycle assessments to enhance sustainability. Overall, sustainable biomaterials play an essential part in direction of environment related issues and advancing healthcare, necessitating continuous innovation and collaboration. Nanotechnology ; Engineering ethics ; Materials science ; Engineering biodegradable polymer synthesis and properties ; Synthesis and properties of polymers ; Graphene and Nanomaterials Applications
Authors: Sarah‐Luise Abram | Isabella Tavernaro | Linda J. Johnston | Shan Zou | Ute Resch‐Genger
Source: Analytical and Bioanalytical Chemistry (review, 2025-01-04)
Field: Chemistry — Analytical Chemistry

Nanoscale reference and test materials for the validation of characterization methods for engineered nanomaterials — current state, limitations, and needs The rational design of engineered nanomaterials (NMs) with improved functionality and their increasing industrial application requires reliable, validated, and ultimately standardized characterization methods for their application-relevant, physicochemical key properties such as size, size distribution, shape, or surface chemistry. This calls for nanoscale (certified) reference materials (CRMs; RMs) and well-characterized reference test materials (RTMs) termed also quality control (QC) samples, assessed, e.g., in interlaboratory comparisons, for the validation and standardization of commonly used characterization methods. Thereby, increasing concerns regarding potential risks of NMs are also addressed and the road for safe and sustainable-by-design concepts for the development of new functional NMs and their use as nanomedicines is paved. With this respect, we will provide an overview of relevant international standardization and regulatory activities, definitions, and recommendations on characterization methods and review currently available organic or inorganic nanoscale CRMs, RMs, and RTMs, including their characterization or certification. In addition, we will highlight typical applications to streamline the regulatory approval process and improve manufacturability including the special challenges imposed by the colloidal nature and sometimes limited stability of NMs. Subsequently, we will critically assess the limitations of currently available nanoscale RMs and RTMs and address the gaps to be filled in the future such as the availability of NMs that come with reference data on properties other than commonly addressed particle size, such as surface chemistry or particle number concentration, or more closely resemble commercially available formulations or address application-relevant matrices. Characterization (materials science) ; Nanotechnology ; Certified reference materials ; Standardization ; Computer science ; Certification ; Materials science ; Chemistry Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Radioactive element chemistry and processing
Authors: Hamid Haj Seyyed Javadi | Anne‐Catherine Lehnen | Matthias Hartlieb
Source: Angewandte Chemie International Edition (review, 2025-04-16)
Field: Chemistry — Organic Chemistry

Bioinspired Cationic Antimicrobial Polymers Antibiotics are an essential tool of modern medicine, which is critically endangered by the spread of antimicrobial resistance (AMR). Without effective antibiotics, a number of medical advancements from the last century are in jeopardy, threatening our global public health and leading to a high death toll. To counteract this development, new therapeutic strategies, that are insusceptible to resistance development have to be established. Among them, antimicrobial polymers (AP)s are a promising class of materials. Their mode-of-action is highly unspecific as they kill bacteria by membrane permeabilization or precipitation of intracellular components. As such, it is unlikely for APs to be affected by AMR. This review highlights recent advances in AP design and understanding of structure-property relationships of these cationic macromolecules. One spotlight is on the polymeric architecture and how it influences AP bioactivity. A second highlight is stimuli-responsive APs and their potential to increase AP selectivity. Moreover, synergistic effects, e.g., between polymer and antibiotics are reviewed. The last focus is on in vivo applications of APs, which could pave a way toward clinical applications. Antimicrobial ; Antibiotics ; Antibiotic resistance ; Antimicrobial peptides ; Nanotechnology ; Chemistry ; Biology ; Microbiology ; Materials science Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Orthopedic Infections and Treatments
Authors: Tim Van Den Bossche | Jean Armengaud | Dirk Benndorf | J. Alfredo Blakeley‐Ruiz | Madita Brauer | Kai Cheng | Marybeth Creskey | Daniel Figeys | Lucia Grenga | Timothy J. Griffin | Céline Henry | Robert L. Hettich | Tanja Holstein | Pratik Jagtap | Nico Jehmlich | Jonghyun Kim | Manuel Kleiner | Benoît J. Kunath | Xuxa Malliet | Lennart Martens | Subina Mehta | Bart Mesuere | Zhibin Ning | Alessandro Tanca | Sergio Uzzau | Pieter Verschaffelt | Jing Wang | Paul Wilmes | Xu Zhang | Xin Zhang | Leyuan Li | The Metaproteomics Initiative
Source: iMeta (review, 2025-05-06)
Field: Chemistry — Spectroscopy

The microbiologist's guide to metaproteomics Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches. Metaproteomics ; Medicine ; Medical physics ; Chemistry ; Metagenomics Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Metabolomics and Mass Spectrometry Studies
Authors: Cameron S. Movassaghi | Jie Sun | Yuming Jiang | Natalie Turner | Vincent Chang | Nara Chung | Ryan Chen | Elizabeth N. Browne | Chuwei Lin | Devin K. Schweppe | Stacy A. Malaker | Jesse G. Meyer
Source: Analytical Chemistry (review, 2025-02-25)
Field: Chemistry — Spectroscopy

Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics Mass spectrometry-based proteomics is about 35 years old, and recent progress appears to be speeding up across all subfields. In this review, we focus on advances over the last two years in select areas within bottom-up proteomics, including approaches to high-throughput experiments, data analysis using machine learning, drug discovery, glycoproteomics, extracellular vesicle proteomics, and structural proteomics. Chemistry ; Mass spectrometry ; Proteomics ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Biosensors and Analytical Detection
Authors: Jin Yao | Yubin Fan | Yunhui Gao | Rong Lin | Zhihui Wang | Mu Ku Chen | Shumin Xiao | Din Ping Tsai
Source: Light Science & Applications (article, 2025-01-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nonlocal Huygens’ meta-lens for high-quality-factor spin-multiplexing imaging Combining bright-field and edge-enhanced imaging affords an effective avenue for extracting complex morphological information from objects, which is particularly beneficial for biological imaging. Multiplexing meta-lenses present promising candidates for achieving this functionality. However, current multiplexing meta-lenses lack spectral modulation, and crosstalk between different wavelengths hampers the imaging quality, especially for biological samples requiring precise wavelength specificity. Here, we experimentally demonstrate the nonlocal Huygens' meta-lens for high-quality-factor spin-multiplexing imaging. Quasi-bound states in the continuum (q-BICs) are excited to provide a high quality factor of 90 and incident-angle dependence. The generalized Kerker condition, driven by Fano-like interactions between q-BIC and in-plane Mie resonances, breaks the radiation symmetry, resulting in a transmission peak with a geometric phase for polarization-converted light, while unconverted light exhibits a transmission dip without a geometric phase. Enhanced polarization conversion efficiency of 65% is achieved, accompanied by a minimal unconverted value, surpassing the theoretical limit of traditional thin nonlocal metasurfaces. Leveraging these effects, the output polarization-converted state exhibits an efficient wavelength-selective focusing phase profile. The unconverted counterpart serves as an effective spatial frequency filter based on incident-angular dispersion, passing high-frequency edge details. Bright-field imaging and edge detection are thus presented under two output spin states. This work provides a versatile framework for nonlocal metasurfaces, boosting biomedical imaging and sensing applications. Optics ; Physics ; Multiplexing ; Polarization (electrochemistry) ; Wavelength ; Computer science ; Telecommunications Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Huanxin Huo | Jingjie Shen | Jianyong Wan | Haoran Shi | Hongxing Yang | Xin Duan | Yihong Gao | Yumeng Chen | Feng Kuang | Hongshan Li | Long Yang | Guanben Du
Source: Nature Communications (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

A tough and robust hydrogel constructed through carbon dots induced crystallization domains integrated orientation regulation Tough hydrogels show great potential applied in flexible electronics, sensors and soft robotics, but it remains challenging to combine high strength, toughness and stability. Here, we report the use of carbon dots (CDs) to induce the formation of crystalline domains, to give materials with favourable properties. The CDs act as nanoscale nucleation-sites within polyvinyl alcohol hydrogels, forming dense crystalline domains that serve as physical crosslinking sites. These domains enable a "pinning effect" that enhances energy dissipation and restricts crack propagation. The resulting hydrogels exhibit strong mechanical performance, including tensile strength up to 156 MPa and toughness of 225 MJ m<sup>-3</sup>, while also maintaining good swelling resistance. This strategy is generalizable across different types of CDs and polymer systems. In addition, the hydrogels demonstrate stable conductivity under water, making them suitable for applications in underwater motion sensing and flexible supercapacitors. This work provides a scalable approach to engineer robust, multifunctional hydrogels. Self-healing hydrogels ; Materials science ; Toughness ; Ultimate tensile strength ; Nanotechnology ; Crystallization ; Nanoscopic scale ; Composite material ; Nucleation ; Delamination (geology) ; Supercapacitor ; Polymer ; Chemical engineering ; Polymer chemistry Carbon and Quantum Dots Applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: A.S. Sethulekshmi | Asok Aparna | Pradeep Parvathi | Rakshit Pathak | Vinay Deep Punetha | Manickam Selvaraj | Appukuttan Saritha
Source: Chemical Engineering Journal (article, 2025-05-03)
Field: Materials Science — Materials Chemistry

Advances in doped carbon quantum dots: synthesis, mechanisms, and applications in sensing technologies Carbon quantum dots ; Quantum dot ; Nanotechnology ; Doping ; Carbon fibers ; Materials science ; Optoelectronics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Jacob R. Shaw | Nicholas Caprio | Nhu Truong | Mehari M Weldemariam | Anh Q. Tran | Nageswara Pilli | Swarnima Pandey | Jace W. Jones | Maureen A. Kane | Ryan M. Pearson
Source: Nature Communications (article, 2025-01-22)
Field: Materials Science — Biomaterials

Inflammatory disease progression shapes nanoparticle biomolecular corona-mediated immune activation profiles Polymeric nanoparticles (NPs) are promising tools used for immunomodulation and drug delivery in various disease contexts. The interaction between NP surfaces and plasma-resident biomolecules results in the formation of a biomolecular corona, which varies patient-to-patient and as a function of disease state. This study investigates how the progression of acute systemic inflammatory disease influences NP corona compositions and the corresponding effects on innate immune cell interactions, phenotypes, and cytokine responses. NP coronas alter cell associations in a disease-dependent manner, induce differential co-stimulatory and co-inhibitory molecule expression, and influence cytokine release. Integrated multi-omics analysis of proteomics, lipidomics, metabolomics, and cytokine datasets highlight a set of differentially enriched TLR4 ligands that correlate with dynamic NP corona-mediated immune activation. Pharmacological inhibition and genetic knockout studies validate that NP coronas mediate this response through TLR4/MyD88/NF-κB signaling. Our findings illuminate the personalized nature of corona formation under a dynamic inflammatory condition and its impact on NP-mediated immune activation profiles and inflammation, suggesting that disease progression-related alterations in plasma composition can manifest in the corona to cause unintended toxicity and altered therapeutic efficacy. Immune system ; Cytokine ; Metabolomics ; Inflammation ; Proteomics ; TLR4 ; Innate immune system ; Lipidomics ; Disease ; Cell biology ; Biology ; Chemistry ; Immunology ; Computational biology ; Bioinformatics ; Medicine ; Biochemistry ; Gene Nanoparticle-Based Drug Delivery ; Blood transfusion and management ; Inflammatory Biomarkers in Disease Prognosis
Authors: Kangming Li | Andre Niyongabo Rubungo | X. L. Lei | Daniel Persaud | Kamal Choudhary | Brian DeCost | Adji Bousso Dieng | Jason Hattrick‐Simpers
Source: Communications Materials (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

Probing out-of-distribution generalization in machine learning for materials Abstract Scientific machine learning (ML) aims to develop generalizable models, yet assessments of generalizability often rely on heuristics. Here, we demonstrate in the materials science setting that heuristic evaluations lead to biased conclusions of ML generalizability and benefits of neural scaling, through evaluations of out-of-distribution (OOD) tasks involving unseen chemistry or structural symmetries. Surprisingly, many tasks demonstrate good performance across models, including boosted trees. However, analysis of the materials representation space shows that most test data reside within regions well-covered by training data, while poorly-performing tasks involve data outside the training domain. For these challenging tasks, increasing training size or time yields limited or adverse effects, contrary to traditional neural scaling trends. Our findings highlight that most OOD tests reflect interpolation, not true extrapolation, leading to overestimations of generalizability and scaling benefits. This emphasizes the need for rigorously challenging OOD benchmarks. Generalization ; Distribution (mathematics) ; Computer science ; Artificial intelligence ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Carlota Bozal‐Ginesta | Sergio Pablo‐García | Changhyeok Choi | Albert Tarancón | Alán Aspuru-Guzik
Source: Nature Reviews Chemistry (review, 2025-07-18)
Field: Materials Science — Materials Chemistry

Developing machine learning for heterogeneous catalysis with experimental and computational data Throughput ; Field (mathematics) ; Computer science ; Machine learning ; Experimental data ; Heterogeneous catalysis ; Computational model ; Artificial intelligence ; Catalysis ; Biochemical engineering ; Chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Serhii Tretiakov | AkshatKumar Nigam | Robert Pollice
Source: Chemical Reviews (review, 2025-06-09)
Field: Materials Science — Materials Chemistry

Studying Noncovalent Interactions in Molecular Systems with Machine Learning Noncovalent interactions (NCIs) is an umbrella term for a multitude of typically weak interactions within and between molecules. Despite the low individual energy contributions, their collective effect significantly influences molecular behavior. Accordingly, understanding these interactions is crucial across fields like catalysis, drug design, materials science, and environmental chemistry. However, predicting NCIs is challenging, requiring at least molecular mechanics-level pairwise energy contributions or efficient quantum mechanical electron correlation treatment. In this review, we investigate the application of machine learning (ML) to study NCIs in molecular systems, an emerging research field. ML excels at modeling complex nonlinear relationships, and is capable of integrating vast data sets from experimental and theoretical sources. It offers a powerful approach for analyzing interactions across scales, from small molecules to large biomolecular assemblies. Specifically, we examine data sets characterizing NCIs, compare molecular featurization techniques, assess ML models predicting NCIs explicitly, and explore inverse design approaches. ML enhances predictive accuracy, reduces computational costs, and reveals overlooked interaction patterns. By identifying current challenges and future opportunities, we highlight how ML-driven insights could revolutionize this field. Overall, we believe that recent proof-of-concept studies foreshadow exciting developments for the study of NCIs in the years to come. Pairwise comparison ; Chemistry ; Non-covalent interactions ; Field (mathematics) ; Nanotechnology ; Biochemical engineering ; Data science ; Computer science ; Molecule ; Artificial intelligence ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Pengfei Jiang | Yan Dai | Yujun Hou | Joshua S. Stein | Siqi Lin | C.W. Zhou | Yannan Hou | Rongrong Zhu | Ki‐Bum Lee | Letao Yang
Source: BMEMat (article, 2025-03-25)
Field: Materials Science — Materials Chemistry

Artificial intelligence‐assisted design, synthesis and analysis of smart biomaterials Abstract Smart biomaterials that can self‐adapt or respond to microenvironmental factors or external signals hold excellent potential for a variety of biomedical applications, from biosensing, drug delivery, and cell therapy to tissue engineering. The complexity of smart biomaterials, including the rational design of their structure and composition, the accurate analysis and prediction of their properties, and the automatic and scale‐up synthesis remains a critical challenge but can be addressed by the recent rise of artificial intelligence (AI). To bridge the literature gap, the current mini‐review will introduce the background of why marrying AI with smart biomaterials is essential and how biomaterial scientists can integrate machine learning (ML) and AI for the discovery, design, analysis, and synthesis of smart biomaterials. For this purpose, the basic principles of ML and AI will first be introduced so that biomaterial scientists can use ML and AI as a tool for basic research. Next, representative examples of using AI to high throughput screen and establish big data of structure‐function relationship of smart biomaterials responding to both chemical, biological, and physical signals. Most importantly, the applications of the AI‐designed or AI‐discovered biomaterials will be overviewed, with a focus on the field of tissue engineering. Lastly, new directions, such as robot‐chemists‐assisted fabrication of biomaterials will be highlighted. Taken together, by engaging biomaterial scientists with the most recent updates in AI material science, we expect to observe continuous growth of the field of AI for science and benefit clinical translation of smart biomaterials for treating a variety of diseases. Computer science ; Engineering ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials