Liposome-aptamerconjugates are powerful tools for bioanalysisand drug delivery by taking advantage of aptamer binding specificityand liposome encapsulation capabilities. Here, we developed multipurposeconjugates that specifically and effectively bind to his-tags anddemonstrated their capability as screening tool for his-tagged proteinsand as signaling tool for bioassays. Using the poly histidine-tag(his-tag) binding aptamer NDM1-H14–01 (his-Apt), we studiedaptamer-liposome immobilization with special focus on retaining aptamerfunctionality and liposome integrity. This involved various strategiesfor modifying liposomes with aptamers, i.e., cholesterol-functionalizedaptamers were inserted into the liposome pre- or post-synthesis, amine-modifiedaptamers were covalently coupled to COOH-groups on the liposome surface,and biotinylated aptamers were incubated with streptavidin-modifiedliposomes, respectively. Extensive characterization of liposome andaptamer properties, as well as conjugate functionality via heterogeneousbinding assays, proved post-insertion and biotinylated aptamers successfulwith a stability of at least 4 months at 4 °C, whereas the othermethods destabilized the aptamer structure. The successful conjugateswere further optimized to (i) serve as a tool for easy detection andscreening of his-tag accessibility in recombinant proteins, whichcan replace common metal-based formats (ii) enable efficient site-directedimmobilization of proteins onto liposomes while omitting the needof additional protein modifications, and (iii) demonstrate that aptamerscan function as a reliable secondary recognition element for protein-modifiedliposomes, e.g., as internal control system. Consequently, these highlyfunctional and stable his-Apt-liposome conjugates represent a versatileplatform technology for bioassays, which can substitute labor- andcost-intensive measurements, as well as improve assay control andreproducibility.

The molecular characterization of extracellular vesicles(EVs)remains a major analytical challenge, particularly regarding lipidcomponents that govern vesicle stability, biogenesis, and biologicalactivity. Here we present an integrated workflow combining high-performanceliquid chromatography with diode array detection (HPLC-DAD), attenuatedtotal reflection Fourier transform infrared spectroscopy (ATR-FTIR),and gas chromatography–mass spectrometry (GC–MS) toprofile EV lipids from both mammalian (human embryonic kidney HEK293Thuman cell line) and microalgal (Tetraselmis chuii) cell-derived EVs. Quantitative phospholipid analysis by HPLC-DADand fatty acid profiling by GC–MS revealed phosphatidylserineas a conserved and selectively enriched lipid signature in EVs acrossphylogenetically distant species, along with system-specific differencesin acyl chain saturation. FTIR spectroscopy was extended beyond conventionalbiochemical fingerprinting to derive spectroscopic indicators (includingprotein-to-lipid ratio, saturated/unsaturated balance, acyl chainlength, and lateral packing) that are effectively related to EV sources.This multitechnique approach provides comprehensive structural readoutsthat translate complex molecular diversity into reproducible descriptorsof extracellular vesicle identity and function. Our findings alsoidentify distinctive lipid and spectroscopic signatures in microalgalEVs, reinforcing their potential as sustainable nanobiotechnologicalplatforms.

Carbon dioxide (CO2) capture and storage represents an important technological challenge. A mechanistic understanding of interactions involved in the capture process is necessary not only for technological development but also for efficient conversion of captured CO2 into value-added materials. Herein, we present a novel contained secondary electrospray ionization platform for studying the interactions of gaseous amines and CO2 gas under microdroplet reaction conditions, which enables mass spectrometry (MS) characterization of CO2 capture products and intermediates in real time. We detected [2 M + CO2 + H]+ species, which corresponds to a six-membered intermediate. DFT calculations confirmed the high stability of the protonated six-membered ring intermediate. This finding provides a plausible concerted mechanism in the microdroplet environment that excludes the involvement of thermodynamically disfavored ionic species. The carbamic acid counterpart of the final product/salt was readily characterized by tandem MS. The carbamic acid/amine salt was also isolated and characterized by Fourier transform infrared spectroscopy. By virtue of the fact that headspace vapors of amines are sampled, we were able to establish a high-throughput platform that enabled the CO2 capture capacity of five different amines to be studied in under 2 min. The same device also enabled the absolute quantification of capture capacity.

Viscoelastic microfluidics (VEM) offers superior biological particle isolation but is hindered by complex dynamics necessitating laborious empirical optimization. To address this challenge, we propose a machine learning (ML)-driven strategy for the rapid identification of optimal operating conditions. By leveraging an experimentally calibrated theoretical model, we generated large-scale particle motion data sets to train a Random Forest algorithm, establishing a robust bidirectional mapping between sorting-relevant dynamic parameters and external input variables. Furthermore, Pareto optimization was integrated to elucidate intrinsic trade-offs among competing objectives, enabling the direct determination of optimal parameters tailored to specific separation requirements. This framework successfully transitions VEM-based isolation from an experience-dependent to a data-driven paradigm. By streamlining the optimization process, our strategy significantly enhances the scalability and utility of VEM for critical clinical applications.

Epilepsy is a chronic neurological disorder characterized by recurrent seizures, which poses a significant threat to human health. Increasing evidence indicates that the pathological mechanism of epilepsy is closely related to the presence of peroxynitrite (ONOO-). To address this issue, we have developed a dual-molecule release precursor, DVN, with different functional components, which consists of a responsive group, a reporter segment, and a drug molecule, for monitoring peroxynitrite. Based on in vitro assays, DVN exhibits several advantages, including high specificity, a rapid response time (120 s), and a dual release function, and is not affected by other reactive oxygen species. When this precursor is activated by peroxynitrite, valproic acid (VPA) is released, which is a broad-spectrum antiepileptic drug that can reduce seizure occurrences; a near-infrared (NIR) reporter unit is formed synchronously using DVN, and we successfully tracked the fluctuations of endogenous and exogenous ONOO- in SH-SY5Y cells and achieved significant spatiotemporal resolution. Moreover, DVN can clearly display the changes of endogenous ONOO- in SH-SY5Y cells. Crucially, DVN helps to perform fluorescence imaging of the ONOO- concentration in epilepsy models, while also reducing the seizure levels in mice and shortening their seizure latency, for the first time. These findings indicate that DVN provides a promising tool for the diagnosis and treatment of epilepsy by imaging the fluctuations of ONOO-, and offers a reliable method for studying the treatment of epilepsy.

The integration of distinct functionalities into single covalent organic frameworks (COFs) is highly desirable for advanced sensing, yet remains synthetically challenging. Herein, we address this by designing and synthesizing donor-acceptor COFs (named COF-Z) from tailored triazine and thiophene derivatives. COF-Z simultaneously exhibits strong intrinsic fluorescence and high peroxidase-like nanozyme activity, a dual functionality achieved through its precise π-conjugated and electron-rich porous structure. This unique combination enables the development of a sensitive dual-mode sensor for tetracycline (TC) detection. The sensing mechanism relies on the specific interaction between TC and the COFs, which enhances fluorescence while inhibiting nanozyme activity, thereby producing complementary optical and colorimetric signals. Furthermore, to facilitate practical application, COF-Z is embedded into an electrospun nanofibrous membrane, creating a portable solid-state sensor that allows for on-site visual detection of TC. When coupled with an artificial neural network for automated image analysis, this integrated platform achieves accurate and intelligent TC quantification. This work not only presents a rational design strategy for multifunctional COFs but also demonstrates a smart, device-ready sensing system for environmental antibiotic monitoring.

Self-powered sensing based on enzymatic biofuel cells (EBFCs) provides a promising platform for miniaturized and portable exosome analysis by converting biochemical reactions directly into electrical output. However, the operational reliability of such systems remains fundamentally restricted by the instability, limited electron-transfer efficiency, and high cost of natural enzymes. Herein, we design a heteroatomic nanozyme featuring Fe single atoms electronically coupled to Au nanoparticles anchored on nitrogen-doped carbon (FeSA-AuNP/NC) to enhance glucose oxidation. When paired with a FeSA/NC cathode, FeSA-AuNP/NC serves as an efficient nanozymatic anode to construct a fully nanozyme-driven BFC. The fully nanozymic BFC displays markedly higher electrical output and improved operational durability relative to the BFC using a GOx-based bioanode. As a proof of concept, this fully nanozymatic system enables quantitative and sensitive exosome detection across a wide linear range (103-107 particles mL-1) with an ultralow detection limit of 362 particles mL-1, highlighting the promise of electronically regulated interfaces to advance next-generation self-powered biosensing platforms.

The reaction efficiency of solution-phase DNA cascade amplifiers is fundamentally constrained by stochastic Brownian motion, creating a thermodynamic diffusion bottleneck that limits sensitivity and speed. To overcome this, we present a general kinetic engineering framework based on spatially confined entropy reduction. By integrating a DNA tetrahedron scaffold with a cascade amplification circuit, we successfully transition the reaction from a stochastic dilute-phase regime to a deterministic pseudosolid-phase regime. For the first time, we employ phase-space kinetic mapping to visualize the entropy-driven acceleration in DNA nanomachines. This comprehensive analysis reveals that this architecture increases the effective local concentration by 4 orders of magnitude by prepaying the entropic cost of molecular collision. Using targets spanning small molecules (BPA), macromolecules (Thrombin), and nucleic acids (miRNA), the system achieved a detection limit of 0.1 pM with rapid kinetics. This work establishes a theoretical blueprint. Crucially, the phase-space kinetic mapping framework proposed here serves as a universal toolkit for the community, offering a new dimension to evaluate and optimize next-generation DNA cascade amplifiers.

Herein, we report a supramolecular photoelectrochemical (PEC) biosensor based on a signal-polarity-inversion strategy, integrated with bacterial imprinting technology, for the ultrasensitive and selective detection of Staphylococcus aureus (S. aureus). The biosensor is constructed by embedding S. aureus within a bacterial-imprinted polymer (BIP) formed from a host-guest complex between 1,3,6,8-tetraethynylpyrene (TEPy) and cucurbit [7] uril (CB [7]). Remarkably, the interaction between the TEPy@4CB [7] complex and S. aureus triggers a unique photocurrent polarity switch from anodic to cathodic. Mechanistic studies reveal that this inversion stems from the encapsulation of the terminal alkyne group of TEPy by CB [7], which alters the electron transfer pathway between the complex and the bacteria. This SPI mechanism not only amplifies the detection signal but also effectively suppresses false-positive responses in complex matrices through polarity switching. Under optimized conditions, the biosensor exhibits excellent selectivity over competing bacteria and achieves a remarkably low detection limit of 2.76 CFU/mL. It also demonstrates high applicability in real food samples. Furthermore, molecular docking simulations provide insights into the assembly and sensing mechanisms. This work significantly mitigates interference issues inherent in conventional detection methods, offering a rapid, efficient, and accurate analytical platform for microbial detection in food and environmental monitoring.