jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guliz Ersoy | Maged Henary
Source: Biomolecules (review, 2025-01-14)
Field: Materials Science — Materials Chemistry

Roadmap for Designing Donor-π-Acceptor Fluorophores in UV-Vis and NIR Regions: Synthesis, Optical Properties and Applications Donor acceptor (D-π-A) fluorophores containing a donor unit and an acceptor moiety at each end connected by a conjugated linker gained attention in the last decade due to their conjugated system and ease of tunability. These features make them good candidates for various applications such as bioimaging, photovoltaic devices and nonlinear optical materials. Upon excitation of the D-π-A fluorophore, intramolecular charge transfer (ICT) occurs, and it polarizes the molecule resulting in the 'push-pull' system. The emission wavelengths of fluorophores can be altered from UV-vis to NIR region by modifying the donor unit, acceptor moiety and the π linker between them. The NIR emitting fluorophores with restricted molecular rotations are used in aggregation-induced emission (AIE). D-π-A fluorophores with carboxylic acid and cyano groups are preferred in photovoltaic applications, and fluorophores with large surface area are used for two photon absorbing applications. Herein, we report the synthesis, optical properties, and applications of various D-π-A fluorophores in UV-vis and NIR region. Fluorophore ; Moiety ; Photochemistry ; Conjugated system ; Acceptor ; Linker ; Materials science ; Intramolecular force ; Fluorescence ; Optoelectronics ; Chemistry ; Polymer ; Optics ; Organic chemistry Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Nanoplatforms for cancer theranostics
Authors: Diksha Thakur | Sivakumar Vaidyanathan
Source: Journal of Materials Chemistry C (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Chiral lanthanide complexes in the history of circularly polarized luminescence: a brief summary Chiral lanthanide complexes with circularly polarized luminescence. Lanthanide ; Luminescence ; Materials science ; Circular polarization ; Crystallography ; Photochemistry ; Nanotechnology ; Optics ; Optoelectronics ; Organic chemistry ; Physics ; Ion ; Chemistry Lanthanide and Transition Metal Complexes ; Molecular spectroscopy and chirality ; Magnetism in coordination complexes
Authors: Miguel T. Campos | Laura S. Pires | Fernão D. Magalhães | María José Oliveira | Artur M. Pinto
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Biomaterials

Self-assembled inorganic nanomaterials for biomedical applications Controlled self-assembly of inorganic nanoparticles has the potential to generate complex nanostructures with distinctive properties. The advancement of more precise techniques empowers researchers in constructing and assembling diverse building blocks, marking a pivotal evolution in nanotechnology and biomedicine. This progress enables the creation of customizable biomaterials with unique characteristics and functions. This comprehensive review takes an innovative approach to explore the current state-of-the-art self-assembly methods and the key interactions driving the self-assembly processes and provides a range of examples of biomedical and therapeutic applications involving inorganic or hybrid nanoparticles and structures. Self-assembly methods applied to bionanomaterials are presented, ranging from commonly used methods in cancer phototherapy and drug delivery to emerging techniques in bioimaging and tissue engineering. The most promising <i>in vitro</i> and <i>in vivo</i> experimental results achieved thus far are presented. Additionally, the review engages in a discourse on safety and biocompatibility concerns related to inorganic self-assembled nanomaterials. Finally, opinions on future challenges and prospects anticipated in this evolving field are provided. Nanomaterials ; Nanotechnology ; Materials science Supramolecular Self-Assembly in Materials ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Zhao Zhao | Beining Zhang | Nan Huang | Yang Sun | Xue Huai | Jiacheng Miao | Sa A. Wang | Shiyuan Liu | Yingying Liu | Zhongliang Chen | Wenzheng Zhu | Xiaoyan Zhou
Source: International Journal of Food Science & Technology (article, 2025-01-01)
Field: Materials Science — Biomaterials

Types of nanomaterials commonly used in food packaging, film formation techniques, and recent advances in their applications Abstract This paper provides a comprehensive review of the types of nanomaterials commonly used in food packaging, their film formation techniques, and recent advances in their applications. The focus is on the introduction of various nanomaterials, including nanosilver, nano zinc oxide, nano titanium dioxide, nano silicon dioxide, chitosan, polylactic acid, cellulose, starch, and protein, detailing their types and properties. The paper also explores both widely adopted and emerging film formation techniques, such as casting, solution casting, coating, electrospinning, and centrifugal spinning. Furthermore, it delves into the functionalities and advantages of nanomaterials in food packaging, such as antibacterial effects, improved packaging performance, intelligent indicators, and material biodegradability. The paper concludes with a discussion on the current benefits and challenges associated with the use of nanomaterials in food packaging, underscoring the need for further research into their safety and cost-effectiveness. Food packaging ; Nanomaterials ; Nanotechnology ; Materials science ; Engineering ; Mechanical engineering Nanocomposite Films for Food Packaging
Authors: Víctor M. Serrano‐Martínez | Henoc Pérez‐Aguilar | María Pilar Carbonell‐Blasco | Avelina Garcı́a-Garcı́a | Francisca Arán‐Aís | Elena Orgilés‐Calpena
Source: Polymers (article, 2025-05-12)
Field: Materials Science — Biomaterials

Effect of Acid Hydrolysis Conditions on the Extraction of Cellulose Nanocrystals The quest for sustainable and efficient methods for extracting cellulose nanocrystals (CNCs) remains critical in the development of advanced materials. This study refines the acid hydrolysis process for cellulose derived from rice straw, an abundant agricultural waste, focusing on the relationship between hydrolysis parameters and CNC properties. The research identifies conditions that enhance crystallinity and colloidal stability while effectively transforming amorphous cellulose into nanocrystals. Advanced characterization techniques, including XRD, SEM, TEM, and zeta potential measurements, provided insights into the structural and colloidal properties of CNCs. The findings demonstrate the influence of reaction temperature, time, and acid concentration on CNC quality, with optimized conditions yielding nanocrystals with enhanced stability and well-defined morphology. This study underscores the potential of rice straw as a renewable resource, advancing the circular economy by promoting the use of sustainable materials in high-value applications such as composite reinforcement and coatings. Cellulose ; Crystallinity ; Materials science ; Nanocrystal ; Hydrolysis ; Zeta potential ; Chemical engineering ; Acid hydrolysis ; Colloid ; Amorphous solid ; Extraction (chemistry) ; Nanotechnology ; Organic chemistry ; Chemistry ; Nanoparticle ; Composite material Advanced Cellulose Research Studies
Authors: Fatima Alhosani | Deema Islayem | Shamma Almansoori | Awais Zaka | Laith Nayfeh | Ayman Rezk | Ahmed F. Yousef | Anna‐Maria Pappa | Ammar Nayfeh
Source: Scientific Reports (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Antibiofilm activity of ZnO–Ag nanoparticles against Pseudomonas aeruginosa Biofilm-related infections remain a major concern in clinical settings due to the increasing challenge of antimicrobial resistance to conventional antimicrobial treatments. Surface coatings of nanomaterials that can effectively prevent biofilm formation and disrupt established biofilms are essential to addressing this challenge. In this study, a ZnO-Ag nanocomposite was synthesized via a dry chemical method and characterized using XRD, XPS, TEM, SEM-EDX, and AFM, confirming the presence of highly crystalline and pure ZnO and Ag nanoparticles with sharp nanoscale features. The nanocomposite demonstrated potent antibiofilm activity against Pseudomonas aeruginosa, a common Gram-negative biofilm-forming pathogen. Surface-coated glass slides prevented initial biofilm formation, while treatment with higher nanocomposite concentrations (≥ 0.25 g/L) significantly disrupted pre-formed biofilms and altered biofilm architecture, as shown by SEM and crystal violet assays. Mechanistic investigations suggested that nanoparticle surface sharpness may contribute to membrane disruption, and EPR analysis confirmed the generation of reactive oxygen species (ROS), particularly superoxide and methyl radicals, under light exposure. These results highlight the composite's strong potential for integration into surfaces prone to bacterial colonization, offering a practical approach for reducing biofilm-related complications. Pseudomonas aeruginosa ; Nanoparticle ; Microbiology ; Chemistry ; Nanotechnology ; Biology ; Materials science ; Bacteria ; Genetics Nanoparticles: synthesis and applications ; Antibiotic Use and Resistance ; Advanced Nanomaterials in Catalysis
Authors: Pallabita Basu | David Crich
Source: Journal of the American Chemical Society (article, 2025-02-05)
Field: Chemistry — Organic Chemistry

The Stereoselectivity of Neighboring Group-Directed Glycosylation Is Concentration-Dependent The formation of 1,2-<i>trans</i>-glycosides taking advantage of neighboring group participation by stereodirecting esters at the 2-position of glycosyl donors is widely held to be a robust and reliable protocol. Examples abound, however, of cases in which less-than-perfect selectivity is obtained, causing practitioners to survey different esters or resort to alternative strategies in the quest for optimal selectivities and yields. Consideration of the mechanism of neighboring group participation and in particular of the competing process of S<sub>N</sub>2-like glycosylation with activated covalent donors leads to the hypothesis that in cases of imperfect selectivity, more careful attention to reaction concentration and stoichiometry may be beneficial. Three case studies are presented to demonstrate the concentration dependence of neighboring group-directed glycosylation reactions targeting the formation of both 1,2-<i>trans</i>-equatorial and 1,2-<i>trans</i>-axial glycosides. Higher concentrations, whether achieved through increased acceptor:donor stoichiometry or through increased concentration at a fixed stoichiometry, mostly lead to erosion of 1,2-<i>trans</i>-selectivity as the competing S<sub>N</sub>2-like reaction with the covalent donors becomes increasingly important. These observations underline the importance of a rational, mechanism-based approach to glycosylation in general and more importantly suggest a simple approach to enhancing 1,2-<i>trans</i>-selectivity in neighboring group-directed glycosylation reactions displaying less-than-perfect 1,2-<i>trans</i>-selectivity, namely, moving to a different concentration regime. Chemistry ; Stereoselectivity ; Glycosylation ; Group (periodic table) ; Stereochemistry ; Organic chemistry ; Biochemistry ; Catalysis Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis
Authors: Wenjie Liao | An Nguyen | Ping Liu
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Alkali-induced catalytic tuning at metal and metal oxide interfaces Alkali metals have been recognized as effective promoters in heterogeneous catalysis, capable of enhancing catalytic activity and tuning product distributions. Over the past few decades, significant efforts have been made aiming to reveal the mechanisms underlying the promoting effect of alkalis. However, the roles that alkali metals play in the catalytic process remain elusive due to challenges in capturing their catalytic behaviours upon exposure to reactive environments. This review summarizes recent surface science and theoretical studies of alkali (potassium, cesium)-decorated metal and metal oxide model catalysts, revealing the crucial tuning by alkalis of activity and selectivity for CO<sub>2</sub> hydrogenation. The analysis of electronic structures identifies the selective binding mechanism of the positively charged alkali ions on the surface, being able to reduce the surface work function and lead to strong electron polarization on the surfaces. Depending on the alkali-support interaction, the deposition of alkalis can selectively modify the bindings of reaction intermediates involved in CO<sub>2</sub> hydrogenation <i>via</i> the interplay among the ionic, covalent and electrostatic tunings. As a result, CO<sub>2</sub> can be effectively activated and converted into diverse products at the alkali-support interface, ranging from formic acid to methanol and ethanol. The identified selective bond-tuning advances the application of alkalis in promoting catalytic activity and controlling catalytic selectivity at alkali-support interfaces. Alkali metal ; Catalysis ; Oxide ; Selectivity ; Metal ; Chemistry ; Inorganic chemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Jun-Lun Meng | Dong Zhang | Yanru Chen | Meng-Hsuan Lin | Yu‐Ching Liu | Steve R. Roffler | Wen-Wei Lin | Chin‐Yuan Chang | Shey-Cherng Tzou | Tian-Lu Cheng | Hsiao-Chen Huang | Zhiqin Li | Yen-Cheng Lin | Yu-Cheng Su
Source: ACS Nano (article, 2025-01-03)
Field: Materials Science — Biomaterials

pH-Responsive Polyethylene Glycol Engagers for Enhanced Brain Delivery of PEGylated Nanomedicine to Treat Glioblastoma The blood-brain barrier (BBB) remains a major obstacle for effective delivery of therapeutics to treat central nervous system (CNS) disorders. Although transferrin receptor (TfR)-mediated transcytosis is widely employed for brain drug delivery, the inefficient release of therapeutic payload hinders their efficacy from crossing the BBB. Here, we developed a pH-responsive anti-polyethylene glycol (PEG) × anti-TfR bispecific antibody (pH-PEG engager<sup>TfR</sup>) that can complex with PEGylated nanomedicine at physiological pH to trigger TfR-mediated transcytosis in the brain microvascular endothelial cells, while rapidly dissociating from PEGylated nanomedicine at acidic endosomes for efficient release of PEGylated nanomedicine to cross the BBB. The pH-PEG engager<sup>TfR</sup> significantly increased the accumulation of PEGylated nanomedicine in the mouse brain compared to wild-type PEG engager<sup>TfR</sup> (WT-PEG engager<sup>TfR</sup>). pH-PEG engager<sup>TfR</sup>-decorated PEGylated liposomal doxorubicin exhibited an enhanced antitumor effect and extended survival in a human glioblastoma (GBM) orthotopic xenograft mice model. Conditional release of PEGylated nanomedicine during BBB-related receptor-mediated transcytosis by pH-PEG engager<sup>TfR</sup> is promising for enhanced brain drug delivery to treat CNS disorders. Transcytosis ; Nanomedicine ; Drug delivery ; Nanocarriers ; Blood–brain barrier ; Pharmacology ; Polyethylene glycol ; PEG ratio ; Medicine ; Transferrin receptor ; Drug ; Chemistry ; Receptor ; Nanotechnology ; Nanoparticle ; Central nervous system ; Materials science ; Internal medicine ; Endocytosis ; Biochemistry Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanopore and Nanochannel Transport Studies
Authors: Yuan Tian | Tongtong Li | Jianbo Pang | Yumei Zhou | Dezhen Xue | Xiangdong Ding | Turab Lookman
Source: npj Computational Materials (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Materials design with target-oriented Bayesian optimization Materials design using Bayesian optimization (BO) typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions. However, materials often possess good properties at specific values or show effective response under certain conditions. We propose a target-oriented BO to efficiently suggest materials with target-specific properties. The method samples potential candidates by allowing their properties to approach the target value from either above or below, minimizing experimental iterations. We compare the performance of target-oriented BO with that of other BO methods on synthetic functions and materials databases. The average results from hundreds of repeated trials demonstrate target-oriented BO requires fewer experimental iterations to reach the same target, especially when the training dataset is small. We further employ the method to discover a thermally-responsive shape memory alloy Ti0.20Ni0.36Cu0.12Hf0.24Zr0.08 with a transformation temperature difference of only 2.66 °C (0.58% of the range) from the target temperature in 3 experimental iterations. Our method provides a solution tailored for optimizing target-specific properties, facilitating the accelerated development of materials with predefined properties. Bayesian optimization ; Bayesian probability ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Injection Molding Process and Properties ; Thermal and Kinetic Analysis
Authors: Yu Zhang | Yang Han | Shuai Chen | Ruijie Yu | Xin Zhao | Xiangtian Liu | Kaipeng Zeng | Mengdi Yu | Jidong Tian | Feng Zhu | Xiaokang Yang | Yaohui Jin | Yanyan Xu
Source: Nature Machine Intelligence (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Large language models to accelerate organic chemistry synthesis Chemistry ; Astrobiology ; Computer science ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Chemical Synthesis and Analysis
Authors: Gabe Guo | Tristan Luca Saidi | Maxwell W. Terban | Michele Valsecchi | Simon J. L. Billinge | Hod Lipson
Source: Nature Materials (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models A major challenge in materials science is the determination of the structure of nanometre-sized objects. Here we present an approach that uses a generative machine learning model based on diffusion processes that are trained on 45,229 known structures. The model factors measured the diffraction pattern as well as the relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-sized broadened powder diffraction pattern, we find that our model, PXRDnet, can successfully solve the simulated nanocrystals as small as 10 Å across 200 materials of varying symmetries and complexities, including structures from all seven crystal systems. We show that our model can successfully and verifiably determine structural candidates four out of five times, with an average error among these candidates being only 7% (as measured by the post-Rietveld refinement R-factor). Furthermore, PXRDnet is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data-driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nanomaterials. Diffraction ; Rietveld refinement ; Nanocrystalline material ; Statistical physics ; Diffusion ; Powder diffraction ; Direct methods ; Computer science ; Ab initio ; Cluster (spacecraft) ; Materials science ; Algorithm ; Nanotechnology ; Crystallography ; Physics ; Thermodynamics ; Chemistry ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Erhai Hu | Hong Han Choo | Wei Zhang | Afriyanti Sumboja | Tribidasari A. Ivandini | Anne Zulfia | Qiang Zhu | Jianwei Xu | Xian Jun Loh | Hongge Pan | Jian Chen | Qingyu Yan
Source: Advanced Functional Materials (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Integrating Machine Learning and Characterization in Battery Research: Toward Cognitive Digital Twins with Physics and Knowledge Abstract The rapid advancement of battery technology has driven the need for innovative approaches to enhance battery management systems. In response, the concept of a cognitive digital twin has been developed to serve as a sophisticated virtual model that dynamically simulates, predicts, and optimizes battery behavior. These models integrate real‐time data with in‐depth physical insights, offering a comprehensive solution for battery management. Fundamental to this development are advanced characterization techniques such as microscopy, spectroscopy, tomography, and electrochemical methods—that provide critical insights into the underlying physics of batteries. Additionally, machine learning (ML) extends beyond predictive analytics to enhance the analytical capabilities. By uncovering deep physical insights, ML significantly improving the accuracy, reliability, and interpretability of these techniques. This review explores how integrating ML with traditional battery characterization techniques bridges the gap between deep physical insights and data‐driven analysis. The synergy not only enhances precision and computational efficiency but also minimizes human intervention, thereby paving the way for more robust and transparent digital twin technologies in battery research. Characterization (materials science) ; Cognition ; Materials science ; Battery (electricity) ; Cognitive science ; Artificial intelligence ; Human–computer interaction ; Nanotechnology ; Computer science ; Physics ; Psychology ; Neuroscience ; Quantum mechanics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; EEG and Brain-Computer Interfaces
Authors: Shun Nanjo | Arifin Arifin | Hayato Maeda | Yoshihiro Hayashi | Kan Hatakeyama‐Sato | Ryoji Himeno | Teruaki Hayakawa | Ryo Yoshida
Source: npj Computational Materials (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines Abstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that incorporates RadonPy, a Python library for fully automated polymer physical property calculations based on all-atom classical molecular dynamics, into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number. Pipeline transport ; On demand ; Atom (system on chip) ; Computer science ; Engineering ; Mechanical engineering ; Embedded system ; Multimedia Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials