jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jaka Snoj | Weijun Zhou | Ajasja Ljubetič | Roman Jerala
Source: Current Opinion in Biotechnology (review, 2025-01-18)
Field: Materials Science — Biomaterials

Advances in designed bionanomolecular assemblies for biotechnological and biomedical applications Recent advances in protein engineering have revolutionized the design of bionanomolecular assemblies for functional therapeutic and biotechnological applications. This review highlights the progress in creating complex protein architectures, encompassing both finite and extended assemblies. AI tools, including AlphaFold, RFDiffusion, and ProteinMPNN, have significantly enhanced the scalability and success of de novo designs. Finite assemblies, like nanocages and coiled-coil-based structures, enable precise molecular encapsulation or functional protein domain presentation. Extended assemblies, including filaments and 2D/3D lattices, offer unparalleled structural versatility for applications such as vaccine development, responsive biomaterials, and engineered cellular scaffolds. The convergence of artificial intelligence-driven design and experimental validation promises strong acceleration of the development of tailored protein assemblies, offering new opportunities in synthetic biology, materials science, biotechnology, and biomedicine. Biochemical engineering ; Nanotechnology ; Computer science ; Computational biology ; Engineering ; Biology ; Materials science Supramolecular Self-Assembly in Materials ; RNA and protein synthesis mechanisms ; RNA modifications and cancer
Authors: Huixia He | Mingzhu Ye | Guizhong Cui | Jianxi Xiao
Source: Materials Today Bio (article, 2025-10-25)
Field: Materials Science — Biomaterials

Recombinant collagen in regenerative medicine: Expression strategies, structural design, and translational applications Recombinant collagen represents a new generation of biomaterials that integrate molecular precision, functional tunability, and scalable biomanufacturing. While animal-derived collagens remain clinically established, their inherent biological variability, limited controllability, and potential pathogen risks have spurred the development of recombinant systems capable of producing collagen with defined sequences and consistent quality. Advances in synthetic biology have enabled expression across diverse hosts-including <i>E. coli</i>, <i>yeast</i>, plants, mammalian cells, and transgenic organisms-each offering distinct advantages in yield, post-translational modification, and triple-helix assembly. Emerging molecular architectures, encompassing triple-helical recombinant collagens, non-helical gelatin-like proteins, and multifunctional fusion constructs, collectively expand the structural repertoire and functional landscape of recombinant collagen-based biomaterials. These engineered materials show strong promise in bone and cartilage regeneration, skin reconstruction, and corneal repair. Nonetheless, challenges remain in achieving complete hydroxylation, cost-effective large-scale manufacturing, and harmonized regulatory standards. The integration of AI-assisted sequence design, programmable molecular engineering, and GMP-compliant production is expected to accelerate clinical translation. By bridging molecular innovation with clinical application, recombinant collagen is poised to redefine the landscape of regenerative medicine and usher in a new era of precision-engineered biomaterials. Recombinant DNA ; Regenerative medicine ; Tissue engineering ; Articular cartilage ; Transgene ; Genetically engineered ; Fusion protein ; Cartilage Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Silk-based biomaterials and applications
Authors: Xiaoying Bi | Kehan Li | Yuanjun Song | Yuan Zou | Peng Xia | Fei Sun | Jing‐Yuan Wu | Xiao‐Yang Zhang | Tong Zhang
Source: Small (review, 2025-09-22)
Field: Materials Science — Materials Chemistry

Negative Photoconductivity in 2D Materials: Revolutionizing Adaptive Optoelectronic Intelligence and Secure Information Architectures 2D materials feature atomically thin layers with strong in-plane covalent bonds and weak out-of-plane van der Waals interactions. This unique structure endows them with unique physical and chemical properties, making them highly promising for photodetector applications. Traditionally, photodetectors have mainly relied on positive photoconductivity (PPC) for their operation. However, recent research has shed light on the importance of anomalous negative photoconductivity (NPC), revealing its significant implications and innovative applications in the field. This review comprehensively summarizes the advancements made in understanding NPC within 2D material photodetectors. It highlights not only the physical mechanisms involved but also their promising applications. Currently, numerous applications of switchable positive-negative photoconductivity, ranging from dynamic perception, image processing, neuromorphic, and bio-inspired systems, information processing, secure encoding, optoelectronic storage, and logic operations, are also included. By delving into these underlying mechanisms and proposing novel application paradigms, the frontiers of 2D material photodetectors are being redefined within the realm of intelligent information electronics. This review delivers not only transformative insights for photodetection technology but also highlights opportunities for next-generation neuromorphic vision systems and cryptophotonic architectures. These breakthroughs provide an innovative hardware foundation crucial for the advancement of future artificial intelligence. Photodetector ; Photoconductivity ; Photodetection ; Neuromorphic engineering ; Photocurrent ; Dissemination ; Nanophotonics 2D Materials and Applications ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Mariah Batool | Sara Pedram | Jasna Janković
Source: ACS Applied Energy Materials (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Advancing Microstructural Analysis: Practical and High-Throughput AI-Based Image Processing and Automated Data Analysis Techniques for Quantification of Structural Parameters for Multicomponent Catalyst Systems With microstructural characterization tools now achieving nanometric and even subatomic resolution, scientists can visualize structures once thought impossible. However, mere visualization underutilizes the vast potential of imaging data. To fully harness this information, researchers are advancing image processing and data analysis techniques that enable precise quantification and automated postcharacterization analysis, unlocking deeper insights into complex material systems. As a rapidly evolving field of research, this Perspective highlights developed practical image and data analysis techniques powered by computer vision (CV) and artificial intelligence (AI) algorithms. These advanced methods offer the capability of unraveling complex material interactions in multicomponent systems, revealing intriguing structural features, and enabling precise quantification. Utilizing data from scanning electron microscopy (SEM), transmission electron microscopy (TEM), and scanning transmission electron microscopy with energy dispersive spectroscopy (STEM/EDS), these methods extract key microstructural features that are critical parameters for optimizing material performance. Tested on diverse electrochemical systems, these techniques demonstrate flexibility in their applications. Their strengths and limitations are discussed, showcasing their potential to drive automated and comprehensive materials characterization and advance the field of material science. Image processing ; Throughput ; Computer science ; Artificial intelligence ; Image (mathematics) ; Data mining ; Materials science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Alberto Marchetto | Monica Tirapelle | Luca Mazzei | Eva Sørensen | Maximilian O. Besenhard
Source: Analytical Chemistry (article, 2025-03-28)
Field: Chemistry — Spectroscopy

In Silico High-Performance Liquid Chromatography Method Development via Machine Learning High-performance liquid chromatography (HPLC) remains the gold standard for analyzing and purifying molecular components in solutions. However, developing HPLC methods is material- and time-consuming, so computer-aided shortcuts are highly desirable. In line with the digitalization of process development and the growth of HPLC databases, we propose a data-driven methodology to predict molecule retention factors as a function of mobile phase composition without the need for any new experiments, solely relying on molecular descriptors (MDs) obtained via simplified molecular input line entry system (SMILES) string representations of molecules. This new approach combines: (a) quantitative structure-property relationships (QSPR) using MDs to predict solute-dependent parameters in (b) linear solvation energy relationships (LSER) and (c) linear solvent strength (LSS) theory. We demonstrate the potential of this computational methodology using experimental data for retention factors of small molecules made available by the research community for which the MDs were obtained via SMILES string representations determined by the structural formulas of the molecules. This method can be adopted directly to predict elution times of molecular components; however, in combination with first-principle-based mechanistic transport models, the method can also be employed to optimize HPLC methods in-silico. Both options can reduce the experimental load and accelerate HPLC method development significantly, lowering the time and cost of the drug manufacturing cycle and reducing the time to market. Given the growing number and quality of HPLC databases, the predictive power of this methodology will only increase in the coming years. Chemistry ; In silico ; Chromatography ; High-performance liquid chromatography ; Hydrophilic interaction chromatography ; Biochemistry Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Bingbing Xu | Yue Zhang | Jia Li | Boxiang Wang | Yanhua Lu | Dehong Cheng
Source: Materials (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Preparation and Applications of Multifunctional MXene/Tussah Silk Fabric The development of functional textiles has become a key focus in recent years, aiming to meet the diverse requirements of modern society. MXene has excellent conductivity, hydrophilicity, and UV resistance, and is widely used in electromagnetic shielding, sensors, energy storage, and photothermal conversion. Tussah silk (TS) is a unique natural textile raw material and has a unique jewelry luster, natural luxury, and a smooth and comfortable feel. However, there are relatively few studies on the functional finishing of TS fabric with Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene. Here, we developed a multifunctional MXene/tussah silk (MXene/TS) fabric by the deposition of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene sheets on the surface of TS fabric through a simple padding-drying-curing process. The obtained MXene/TS fabric (five cycles) exhibited excellent conductivity (4.8 S/m), air permeability (313.6 mm/s), ultraviolet resistance (ultraviolet protection factor, UPF = 186.3), photothermal conversion (temperature increase of 11 °C), and strain sensing. Thanks to these superior properties, the MXene/TS fabric has broad application prospects in motion monitoring, smart clothing, flexible wearables, and artificial intelligence. Materials science ; SILK ; Textile ; Composite material ; Air permeability specific surface ; Nanotechnology ; Layer (electronics) MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Silk-based biomaterials and applications
Authors: Leah Okoruwa | Fatih Tarak | Farzaneh Sameni | Ehsan Sabet
Source: Polymers (article, 2025-01-15)
Field: Chemistry — Organic Chemistry

Bridging Experimentation and Computation: OMSP for Advanced Acrylate Characterization and Digital Photoresin Design in Vat Photopolymerization Vat photopolymerization (VPP) is an additive manufacturing method that requires the design of photocurable resins to act as feedstock and binder for the printing of parts, both monolithic and composite. The design of a suitable photoresin is costly and time-consuming. The development of one formulation requires the consumption of kilograms of costly materials, weeks of printing and performance testing, as well as the need to have developers with the expertise and knowledge of the materials used, making the development process cost thousands. This paper presents a new characterization methodology for acrylates that allows for the computerization of the photoresin formulation development process, reducing the timescale to less than a week. Okoruwa Maximum Saturation Potential (OMSP) is a methodology that uses attenuated total reflection (ATR-FTIR) to study the functional group of acrylates, assigning numerical outputs to characterize monomers, oligomers and formulations, allowing for more precise distinguishment between materials. It utilizes the principles of Gaussian normal distribution for the storage, recall, and computerization of acrylate data and formulation design without the need to database numerous files of spectral data to an average coefficient of determination (R<sup>2</sup>) of 0.97. The same characterization method can be used to define the potential reactivity of acrylate formulations without knowing the formulation components, something not possible when using properties such as functionality. This allows for modifications to be made to unknown formulations without prior knowledge of their contents. Validation studies were performed to define the boundaries of the operation of OMSP and assess the methodology's reliability as a characterization tool. OMSP can confidently detect changes caused by the presence of various acrylates made to the photoresin system and distinguish between acrylates of the same viscosity and functionality. OMSP can compare digitally mixed formulations to physically mixed formulations and provides a high degree of accuracy (R<sup>2</sup> of 0.9406 to 0.9964), highlighting the future potential for building foundations for artificial intelligence in VPP; the streamlining of photoresin formulation design; and transforming the way acrylates are characterized, selected, and used. Acrylate ; Computer science ; Characterization (materials science) ; Process engineering ; Photopolymer ; Raw material ; Process (computing) ; Materials science ; Monomer ; Nanotechnology ; Engineering ; Polymer ; Composite material ; Chemistry ; Organic chemistry Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Additive Manufacturing and 3D Printing Technologies
Authors: Pengyu Wang | Weiwei Chen | Jingfeng Zhang | Chunshu Pan | Yagui Lv | Yan Sun | Yanan Wang | Xuehua Ma | Changyong Gao | Tianxiang Chen | Aiguo Wu | Jianjun Zheng
Source: Nanomedicine (review, 2025-03-20)
Field: Materials Science — Biomaterials

Advances in the treatment of atherosclerotic plaque based on nanomaterials Atherosclerosis is the leading cause of cardiovascular disease worldwide, posing not only a significant threat to cardiovascular health but also impairing the function of multiple organs, with severe cases potentially being life-threatening. Consequently, the effective treatment of atherosclerosis is of paramount importance in reducing the mortality associated with cardiovascular diseases. With the advancement of nanomedicine and a deeper understanding of the pathological mechanisms underlying atherosclerosis, nanomaterials have emerged as promising platforms for precise diagnosis and targeted therapeutic strategies. These materials offer notable advantages, including targeted drug delivery, enhanced bioavailability, improved drug stability, and controlled release. This review provides an overview of the mechanisms underlying atherosclerotic plaque development and examines nanomaterial-based therapeutic approaches for managing atherosclerotic plaques, including therapies targeting cholesterol metabolism, anti-inflammatory strategies, macrophage clearance, and immunotherapy. Additionally, the paper discusses the current technical challenges associated with the clinical transformation of these therapies. Finally, the potential future integration of nanomaterials, smart nanomaterials, and artificial intelligence in the treatment of atherosclerosis is also explored. Nanomaterials ; Nanotechnology ; Chemistry ; Medicine ; Materials science Nanoparticle-Based Drug Delivery ; Cerebrovascular and Carotid Artery Diseases ; Nanoplatforms for cancer theranostics
Authors: Le Zhang | Liqiang He | Shuyuan Xu | Haoyu Wang | Yunlong Sun | Yating Ran | Dong Wang | Kaiyun Chen | Junkai Deng | Zibin Chen | Danyang Wang | Michael A. Carpenter | Sen Yang
Source: Advanced Science (article, 2025-06-04)
Field: Materials Science — Ceramics and Composites

Superior Electromechanical Power at Rare‐Earth Manipulated Glassy Morphotropic Phase Transitions High-precision displacement control and the driving joints of artificial intelligence robotics, as well as advanced medical facilities, necessitate the use of superior lead-free electromechanical materials that exhibit substantial electrostrain and driving force while maintaining thermal stability. In this study, an effective physical approach is developed to achieve significant enhancements of 135% and 50% in symmetric electrostrain and elastic modulus, respectively, of Sm-doped (Bi,Na)TiO<sub>3</sub>-BaTiO<sub>3</sub> ceramics within the temperature range of 293-353K. This advancement facilitates a marked improvement in stress output. Unlike the prevalent focus on enhancing symmetric/asymmetric electrostrain output through polar coexistence states and defect dipoles, our approach induces superior electrostrain and stress power by inhibiting the formation of the R3c phase and manipulating the transition pathway in the morphotropic phase boundary composition of (Bi,Na)TiO<sub>3</sub>-based ceramics via rare-earth Sm doping. The achieved reversible glassy P4bm→P4mm and residual R3c→P4mm parallel transition paths, characterized by significant lattice expansion, enable the realization of potential electromechanical power across a broad temperature range. This approach overcomes the limitations of significant elastic softening and the deterioration of electrical properties with temperature variations at specific morphotropic/polymorphic phase transitions. Thus, it offers an effective method for generating superior electromechanical power in lead-free ferroelectrics. Rare earth ; Materials science ; Phase transition ; Phase (matter) ; Condensed matter physics ; Mineralogy ; Chemistry ; Metallurgy ; Physics Glass properties and applications ; Ferroelectric and Piezoelectric Materials ; Phase-change materials and chalcogenides
Authors: Shubhrat Maheshwari | Aditya Singh | Amita Verma | Mohammad Shariq | Juber Akhtar | Omar Awad Alsaidan | Bhupendra G. Prajapati
Source: Medical Oncology (review, 2025-07-16)
Field: Materials Science — Biomaterials

Superparamagnetic iron oxide nanoparticles (SPIONs) in targeting brain tumors: advances and challenges Hematology ; Iron oxide nanoparticles ; Nanoparticle ; Nanotechnology ; Superparamagnetism ; Cancer research ; Chemistry ; Medicine ; Internal medicine ; Materials science ; Physics Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Nisha D. Masane | Arti S. Rathod | Vaibhav G. Akhand | Vinayak A. Katekar | Swati P. Deshmukh
Source: GSC Advanced Research and Reviews (article, 2025-01-25)
Field: Materials Science — Biomaterials

Nanoparticles based drug delivery system for cancer therapy Nanoparticle-based drug delivery systems (NDDS) have emerged as a transformative approach in cancer therapy, offering significant advantages over conventional treatments. This review explores the diverse applications of nanoparticles in cancer therapy, highlighting their role in enhancing drug delivery, reducing systemic toxicity, and overcoming challenges such as drug resistance and tumor heterogeneity. Nanoparticles, including liposomes, polymeric nanoparticles, metallic nanoparticles, and more recently developed systems like carbon nanotubes, dendrimers, and exosomes, are engineered for targeted drug delivery. These nanoparticles improve the pharmacokinetics and bioavailability of anticancer agents, enabling site-specific accumulation through mechanisms such as the enhanced permeability and retention (EPR) effect and active targeting via ligands. Additionally, nanoparticles play a critical role in combination therapies, immunotherapy, and overcoming multidrug resistance (MDR) by bypassing efflux pumps and targeting cancer stem cells (CSCs). Emerging innovations in “smart” nanoparticles, capable of responding to environmental triggers like pH or temperature, as well as their integration with RNA-based therapies and artificial intelligence (AI) for personalized treatment, represent the future direction of cancer nanomedicine. Despite the progress, regulatory challenges, safety concerns, and large-scale manufacturing remain key hurdles. This review provides an overview of the current landscape, challenges, and future prospects of NDDS in cancer therapy, emphasizing their potential to improve clinical outcomes and revolutionize cancer treatment. Drug delivery ; Cancer therapy ; Cancer ; Nanotechnology ; Medicine ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment
Authors: Shihan Kong | Kaiwei Zhu | Cong Wen | Yingnan Li | Fang Wu | Junzhi Yu | Xiaoxia Chang
Source: Science China Chemistry (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

What can AI reciprocally contribute to energy: concept, method, and technology Energy (signal processing) ; Computer science ; Artificial intelligence ; Data science ; Physics Machine Learning in Materials Science
Authors: Raúl E. Martínez-Herrera | Georgia María González-Meza | Edgar Ricardo Meléndez-Sánchez
Source: Process Biochemistry (article, 2025-01-24)
Field: Materials Science — Biomaterials

Toward sustainable bioplastics: The potential of algal biomass in PHA production and biocomposites fabrication Bioplastic ; Biomass (ecology) ; Production (economics) ; Biochemical engineering ; Sustainable production ; Pulp and paper industry ; Polyhydroxyalkanoates ; Fabrication ; Chemistry ; Biotechnology ; Environmental science ; Waste management ; Biology ; Ecology ; Economics ; Engineering biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Microplastics and Plastic Pollution