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1. Nanoplasmonic aptasensor for sensitive, selective, and real-time detection of dopamine from unprocessed whole blood

   https://doi.org/10.1126/sciadv.adp7460  

   Biswas, Aritra; Lee, Sang; Cencillo-Abad, Pablo; Karmakar, Manobina; Patel, Jay; Soudi, Mahdi; Chanda, Debashis (September 2024, Science Advances)

   Neurotransmitters are crucial for the proper functioning of neural systems, with dopamine playing a pivotal role in cognition, emotions, and motor control. Dysregulated dopamine levels are linked to various disorders, underscoring the need for accurate detection in research and diagnostics. Single-stranded DNA (ssDNA) aptamers are promising bioreceptors for dopamine detection due to their selectivity, improved stability, and synthesis feasibility. However, discrepancies in dopamine specificity have presented challenges. Here, we surface-functionalized a nano-plasmonic biosensing platform with a dopamine-specific ssDNA aptamer for selective detection. The biosensor, featuring narrowband hybrid plasmonic resonances, achieves high specificity through functionalization with aptamers and passivation processes. Sensitivity and selectivity for dopamine detection are demonstrated across a wide range of concentrations, including in diverse biological samples like protein solutions, cerebrospinal fluid, and whole blood. These results highlight the potential of plasmonic “aptasensors” for developing rapid and accurate diagnostic tools for disease monitoring, medical diagnostics, and targeted therapies. 

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2. Universal Phase Identification of Block Copolymers From Physics‐Informed Machine Learning

   https://doi.org/10.1002/pol.20241063  

   Fang, Xinyi; Murphy, Elizabeth A; Kohl, Phillip A; Li, Youli; Hawker, Craig J; Bates, Christopher M; Gu, Mengyang (March 2025, Journal of Polymer Science)

   ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials. 

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3. Atomistic Modeling of Microstructural Defect Evolution in Alloys Under Irradiation: A Comprehensive Review

   https://doi.org/10.3390/app15169110  

   Fan, Yue (August 2025, Applied Sciences)

   Developing structural materials capable of maintaining integrity under extreme irradiation conditions is a cornerstone challenge for advancing sustainable nuclear energy technologies. The complexity and severity of radiation-induced microstructural changes—spanning multiple length and timescales—pose significant hurdles for purely experimental approaches. This review critically evaluates recent advancements in atomistic modeling, emphasizing its transformative potential to decipher fundamental mechanisms driving microstructural evolution in irradiated alloys. Atomistic simulations, such as molecular dynamics (MD), have successfully unveiled initial defect formation processes at picosecond scales. However, the inherent temporal limitations of conventional MD necessitate advanced methodologies capable of exploring slower, thermally activated defect kinetics. We specifically traced the development of powerful potential energy landscape (PEL) exploration algorithms, which enable the simulation of high-barrier, rare events of defect evolution processes that govern long-term material degradation. The review systematically examines point defect behaviors in various crystal structures—BCC, FCC, and HCP metals—and elucidates their characteristic defect dynamics, respectively. Additionally, it highlights the pronounced effects of chemical complexity in concentrated solid-solution alloys and high-entropy alloys, notably their sluggish diffusion and enhanced defect recombination, underpinning their superior radiation tolerance. Further, the interaction of extended defects with mechanical stresses and their mechanistic implications for material properties are discussed, highlighting the critical interplay between thermal activation and strain rate in defect evolution. Special attention is dedicated to the diverse mechanisms of dislocation–obstacle interactions, as well as the behaviors of metastable grain boundaries under far-from-equilibrium environments. The integration of data-driven methods and machine learning with atomistic modeling is also explored, showcasing their roles in developing quantum-accurate potentials, automating defect analysis, and enabling efficient surrogate models for predictive design. This comprehensive review also outlines future research directions and fundamental questions, paving the way toward autonomous materials’ discovery in extreme environments. 

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4. Shear-free mixing to achieve accurate temporospatial nanoscale kinetics through scanning-SAXS: ion-induced phase transition of dispersed cellulose nanocrystals

   https://doi.org/10.1039/D0LC01048K  

   Rosén, Tomas; Wang, Ruifu; He, HongRui; Zhan, Chengbo; Chodankar, Shirish; Hsiao, Benjamin S. (January 2021, Lab on a Chip)

   null (Ed.)

   Time-resolved in situ characterization of well-defined mixing processes using small-angle X-ray scattering (SAXS) is usually challenging, especially if the process involves changes of material viscoelasticity. In specific, it can be difficult to create a continuous mixing experiment without shearing the material of interest; a desirable situation since shear flow both affects nanoscale structures and flow stability as well as resulting in unreliable time-resolved data. Here, we demonstrate a flow-focusing mixing device for in situ nanostructural characterization using scanning-SAXS. Given the interfacial tension and viscosity ratio between core and sheath fluids, the core material confined by sheath flows is completely detached from the walls and forms a zero-shear plug flow at the channel center, allowing for a trivial conversion of spatial coordinates to mixing times. With this technique, the time-resolved gel formation of dispersed cellulose nanocrystals (CNCs) was studied by mixing with a sodium chloride solution. It is observed how locally ordered regions, so called tactoids, are disrupted when the added monovalent ions affect the electrostatic interactions, which in turn leads to a loss of CNC alignment through enhanced rotary diffusion. The demonstrated flow-focusing scanning-SAXS technique can be used to unveil important kinetics during structural formation of nanocellulosic materials. However, the same technique is also applicable in many soft matter systems to provide new insights into the nanoscale dynamics during mixing. 

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5. An Extracellular, Ca ²⁺ ‐Activated Nuclease ( EcnA ) Mediates Transformation in a Naturally Competent Archaeon

   https://doi.org/10.1111/mmi.15311  

   Fonseca, Dallas R; Day, Leslie A; Crone, Kathryn K; Costa, Kyle C (October 2024, Molecular Microbiology)

   ABSTRACT Transformation, the uptake of DNA directly from the environment, is a major driver of gene flow in microbial populations. In bacteria, DNA uptake requires a nuclease that processes dsDNA to ssDNA, which is subsequently transferred into the cell and incorporated into the genome. However, the process of DNA uptake in archaea is still unknown. Previously, we cataloged genes essential to natural transformation inMethanococcus maripaludis, but few homologs of bacterial transformation‐associated genes were identified. Here, we characterize one gene, MMJJ_16440 (named here asecnA), to be an extracellular nuclease. We show that EcnA is Ca²⁺‐activated, present on the cell surface, and essential for transformation. While EcnA can degrade several forms of DNA, the highest activity was observed with ssDNA as a substrate. Activity was also observed with circular dsDNA, suggesting that EcnA is an endonuclease. This is the first biochemical characterization of a transformation‐associated protein in a member of the archaeal domain and suggests that both archaeal and bacterial transformation initiate in an analogous fashion. 

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