Restoring three-dimensional electrical conduction in infarcted myocardium remains a critical challenge, as conventional conductive hydrogel patches largely remain surface-confined and prevent electrical coupling of residual cardiomyocytes within fibrotic scars. Here, we present a self-growing conductive volumetric interface (SCOVE) that transforms surface-confined biointerfaces into tissue-integrated, three-dimensional conductive networks. SCOVE is delivered as an injectable hydrogel precursor containing the tissue-permeable conductive monomer 3,4-ethylenedioxythiophene-acetic acid sodium salt (ETE), which rapidly infiltrates infarcted myocardium and undergoes endogenous glucose-triggered oxidative polymerization to self-grow a conductive polyETE network in situ. The resulting hydrogel gels within 1 min, reaches cardiac-mimetic conductivity (∼1 S m- 1) within 45 min, and preserves native myocardial mechanics without inducing tissue stiffening. In a rat myocardial infarction model, SCOVE penetrates the infarct, reduces scar resistivity by 2.54-fold compared with conventional 2D conductive patches, restores electrical coupling among residual cardiomyocytes, enhances Cx43 expression, and accelerates impulse propagation. By replacing static, surface-confined conductive patches with self-growing volumetric biointerfaces, this work establishes a generalizable strategy for reconstructing tissue electrophysiology and advancing bioelectronic therapies for myocardial infarction and other electrically dysfunctional tissues.

Rapid and sensitive detection of foodborne pathogens is essential for ensuring food safety and protecting public health. In this study, we developed an innovative microfluidic fluorescence digital analysis platform enhanced by deep learning to detect pathogens at ultra-low concentrations. The biosensor features a staggered herringbone double-spiral (SHDS) microfluidic design, seamlessly integrating bacteria capture, detection, and release processes using Quantum dot (QD)-Aptamer conjugates for precise identification. Fluorescence image analysis, powered by a Resnet-18-based convolutional neural networks (CNN), directly quantifies Escherichia coli (E. coli) concentrations from fluorescence images, streamlining data processing and increasing sensitivity. The platform offers a linear detection range from 10 to 3 x 106 CFU/mL (R2 = 0.990), achieves capture efficiencies of up to 100 % at low bacterial concentrations (4 x 102 CFU/mL), and offers an ultra-low detection limit of 2 CFU/mL within just 1.5 hours. The CNN model effectively filters out background noise and interferences, achieving over 99 % predictive accuracy. Validation using milk and chicken samples resulted in high recovery rates (96.7 % to 104.0 %). This biosensor presents a rapid, reliable, and practical solution for pathogen detection in complex food matrices, significantly improving food safety and security.

Compared with pure polymeric and inorganic membranes, fillers incorporated hybrid membranes, also known as mixed matrix membranes (MMMs), offer enhanced gas separation capabilities by synergizing the advantageous properties of both matrices and filler materials. In this study, we report the development of a fully organic MMM system composed of polydimethylsiloxane (PDMS) and monodisperse hollow polystyrene (HPS) particles, designed for efficient CO2/CH4 separation. The HPS particles were synthesized via template polymerization, followed by selective template removal to yield uniform, cross-linked spheres with an average diameter of 354 nm. In the pure gas permeation test, compared with neat PDMS membrane, the PDMS/HPS MMM containing 4 wt% HPS particles demonstrated a 259 % increase in CO2 permeability and a 37 % improvement in the ideal CO2/CH4 selectivity. For the CO2/CH4 mixture separation performance test, the MMM demonstrated up to 3.4 times higher CO2 permeability than neat PDMS membranes, with a slight increase in selectivity. The improved performance of gas separation can be attributed to the distinctive hollow structure of the HPS particles, which contributes additional free volume to the membranes, along with the beneficial interface observed between PDMS and HPS particles. These performance enhancements, combined with the use of low-cost, easy solvent-free membrane fabrication and fully organic materials, demonstrate the potential of this approach for industrial gas separation applications such as natural gas purification.

Polycyclic aromatic hydrocarbons (PAHs) are significant environmental contaminants with considerable health risks, emphasizing the need for effective monitoring and identification. On-site detection of PAHs using surface-enhanced Raman scattering (SERS) remains challenging due to their weak adsorption on substrates and potential interference from the substrates themselves. To address these challenges, we developed hollow raspberry-like plasmonic nanoaggregates made of functionalized-polystyrene hollow microspheres (HM) decorated with gold nanoparticles (Au NPs). These nanoaggregates feature a hydrophobic inner cavity that effectively enriches PAHs, improving detection sensitivity. Through enhanced plasmonic coupling by carefully controlling Au NPs coverage on polystyrene (PS) surfaces, functionalizing the amino groups on the microsphere surface, and fine-tuning the Au NP to PS ratio, our method achieved detection limits of 4 x 10-8 M for pyrene, 6 x 10-7 M for fluorene, and 4 x 10-7 M for benzo[a]anthracene. Moreover, this approach was effectively utilized for detecting PAHs in both Yellow River water and tap water. This study highlights the capabilities of hollow raspberry-like plasmonic nanoaggregates for qualitative and quantitative analysis of PAHs, thereby broadening the use of advanced nanomaterials in monitoring environmental water quality.

In response to the growing demand for advanced micro/nano characterization in materials and surface-interface sciences, we report a highly sensitive astigmatism displacement microscopy (ADM) system with subnanometer axial resolution of 0.1 nm. This low-power, noncontact, nondestructive, and rapid optical 3D imaging technique enables large-area characterization with exceptional imaging quality. Using ADM, we achieved high-quality 3D morphology characterization of Au nanoplates, CD-ROMs, semiconductor Si wafers, and few-layer 2D materials (like MoS2). For few-layer 2D materials, ADM demonstrates a linear capability to resolve atomic layer numbers. Additionally, ADM successfully characterized transparent and semitransparent single crystals, even in the presence of inclination-induced interference fringes. The development of ADM provides a powerful tool for micro/nanoscale characterization, with significant implications for materials and surface-interface research.

In the past decades, colloid research has experienced significant growth across various scientific disciplines. This review explores recent advancements in the design and fabrication of cucurbit[n]urils (CB[n])‐mediated self‐assembly for colloids with advanced structures. CB[n], a class of macrocyclic molecules known for their distinctive host–guest interactions, plays a crucial role in synthesizing colloidal superstructures due to their ability to form stable complexes with a wide range of guest molecules. This review provides a comprehensive summary of recent advances in CB[n]‐mediated self‐assembly strategies for constructing diverse colloidal architectures. These assemblies are systematically categorized into distinct types, including colloidal clusters, 1D colloidal chains, raspberry‐like colloids, core–shell colloids, and colloidosomes. While the primary emphasis is placed on construction methodologies, the potential applications of these superstructures are also briefly highlighted in areas such as drug delivery, catalysis, and sensing, to illustrate their relevance in the development of functional materials. By examining the synthesis methods and potential applications, this review underscores the versatility and adaptability of CB[n]‐mediated colloidal systems and their potential to drive future innovations in colloid science.

Microorganisms exhibit remarkable diversity, making their comprehensive characterization essential for understanding ecosystem functioning and safeguarding human health. However, traditional culture-based methods entail inherent limitations for resolving microbial heterogeneity, isolating slow-growing microorganisms, and accessing uncultivated microbes. Conversely, droplet-based microfluidics enables a high-throughput and precise platform for single-bacterium manipulation by physically isolating individual cells within microdroplets. This technology presents a transformative approach to overcoming the constraints of conventional techniques. This review outlines the fundamental principles, recent research advances, and key application domains of droplet-based microfluidics, with a particular focus on innovations in single-bacterium encapsulation, sorting, cultivation, and functional analysis. Applications such as antibiotic susceptibility testing, enzyme-directed evolution screening, microbial interaction studies, and the cultivation of novel bacterial species are discussed, underscoring the technology’s broad potential in microbiological research and biotechnology.

Confocal Raman microscopy is a powerful technique for identifying materials and molecular species; however, the signal from Raman scattering is extremely weak. Typically, handheld Raman instruments are cost-effective but less sensitive, while high-end scientific-grade Raman instruments are highly sensitive but extremely expensive. This limits the widespread use of Raman technique in our daily life. To bridge this gap, we explored and developed a cost-effective yet highly sensitive confocal Raman microscopy system. The key components of the system include an excitation laser based on readily available laser diode, a lens-grating-lens type spectrometer with high throughput and image quality, and a sensitive detector based on a linear charge-coupled device (CCD) that can be cooled down to -30 °C. The developed compact Raman instrument can provide high-quality Raman spectra with good spectral resolution. The 3rd order 1450 cm-1 peak of Si (111) wafer shows a signal-to-noise ratio (SNR) better than 10:1, demonstrating high sensitivity comparable to high-end scientific-grade Raman instruments. We also tested a wide range of different samples (organic molecules, minerals and polymers) to demonstrate its universal application capability.

Conventional solid-based SERS substrates often face challenges with inconsistent sample distribution, while liquid-based SERS substrates are prone to aggregation and precipitation, resulting in irreproducible signals in both cases. In this study, we tackled this dilemma by designing and synthesizing raspberry-like plasmonic nanoaggregates that exhibit a high density of hotspots and are colloidally stable at the same time. In particular, the nanoaggregates consist of a core made of functionalized polystyrene (PS) microspheres, which act as a template for rapid self-assembly of Au@Ag core-shell nanoparticles to form raspberry-like hierarchical nanoaggregates within 5 min of mixing. The optimized nanoaggregates can be used as reproducible and stable SERS substrates for a range of wastewater pollutants (e.g., rhodamine 6G (R6G) and malachite green (MG)) and nucleobases (e.g., adenine and uracil), with the detection limits as low as 1 x 10-10, 1 x 10-16, 3 x 10-8, and 3 x 10-7 M, respectively. Additionally, the trace detection of adenine in clinical urine samples has been successfully demonstrated. Our modular assembly approach opens up new possibilities in SERS substrate design and advanced trace-chemical detection technologies.

Solar-driven CO 2 selective reduction with high conversion is a challenging task yet holds immense promise for both CO 2 neutralization and green fuel production. Enhancing CO 2 adsorption at the catalytic centre can trigger a highly efficient CO 2 capture-to-conversion process. Herein, we introduce cucurbit[n]urils (CB[n]), a new family of molecular ligands, as a key component in the creation of a 3D cage-like metal (nickel, Ni)-complex molecular co-catalyst (CB[7]-Ni) for photocatalysis. It exhibits an unprecedented CO yield rate of 72.1 μmol ⋅ h −1 with a high selectivity of 97.9 % under visible light irradiation. To verify the origin of the carbon source in the products, a straightforward isotopic tracing method is designed based on tandem reactions. The catalytic process commences with photoelectron transfer from Ru(bpy) 3 2+ to the Ni 2+ site, resulting in the reduction of Ni 2+ to Ni + . The locally enriched CO 2 molecules in the cage ligand CB[7] undergo selective reduction by the Ni + nearby to form CO product. This work exemplifies the inspiring potential of ligand structure engineering in advancing the development of efficient unanchored molecular co-catalysts.

Antibiotics act against bacterial pathogens by inhibiting their growth or killing them directly. Different modes of action determine different antibacterial responses, whereas phenotypic differences in bacteria can challenge the efficacy of antibiotics. ABSTRACT With the spread of multidrug-resistant bacteria, there has been an increasing focus on molecular classes that have not yet yielded an antibiotic. A key capability for assessing and prescribing new antibacterial treatments is to compare the effects antibacterial agents have on bacterial growth at a phenotypic, single-cell level. Here, we combined time-lapse microscopy with microfluidics to investigate the concentration-dependent killing kinetics of stationary-phase Escherichia coli cells. We used antibacterial agents from three different molecular classes, β-lactams and fluoroquinolones, with the known antibiotics ampicillin and ciprofloxacin, respectively, and a new experimental class, protein Ψ-capsids. We found that bacterial cells elongated when treated with ampicillin and ciprofloxacin used at their minimum inhibitory concentration (MIC). This was in contrast to Ψ-capsids, which arrested bacterial elongation within the first two hours of treatment. At concentrations exceeding the MIC, all the antibacterial agents tested arrested bacterial growth within the first 2 h of treatment. Further, our single-cell experiments revealed differences in the modes of action of three different agents. At the MIC, ampicillin and ciprofloxacin caused the lysis of bacterial cells, whereas at higher concentrations, the mode of action shifted toward membrane disruption. The Ψ-capsids killed cells by disrupting their membranes at all concentrations tested. Finally, at increasing concentrations, ampicillin and Ψ-capsids reduced the fraction of the population that survived treatment in a viable but nonculturable state, whereas ciprofloxacin increased this fraction. This study introduces an effective capability to differentiate the killing kinetics of antibacterial agents from different molecular classes and offers a high content analysis of antibacterial mechanisms at the single-cell level. IMPORTANCE Antibiotics act against bacterial pathogens by inhibiting their growth or killing them directly. Different modes of action determine different antibacterial responses, whereas phenotypic differences in bacteria can challenge the efficacy of antibiotics. Therefore, it is important to be able to differentiate the concentration-dependent killing kinetics of antibacterial agents at a single-cell level, in particular for molecular classes which have not yielded an antibiotic before. Here, we measured single-cell responses using microfluidics-enabled imaging, revealing that a novel class of antibacterial agents, protein Ψ-capsids, arrests bacterial elongation at the onset of treatment, whereas elongation continues for cells treated with β-lactam and fluoroquinolone antibiotics. The study advances our current understanding of antibacterial function and offers an effective strategy for the comparative design of new antibacterial therapies, as well as clinical antibiotic susceptibility testing.

Colorimetric detection on a microchip frequently suffers the sensitivity problem due to the limited optical path length. Herein, a point-of-care testing (POCT) device based on the colorimetric detection on a microchip with an array of micro through holes for enlarged optical length and an optical imaging system was demonstrated. Each micro through hole has a depth of 2 mm and a volume roughly 0.6 ill, connecting to a microchannel for the efficient reaction. Up to 32 micro through holes were arranged compactly along a round region with a diameter of 13 mm to make them effectively captured by a smartphone camera in a short distance (6.5 cm). For reliable smartphone imaging, a handheld, battery powered optical imaging cassette was made by three-dimensional (3D) printing. A data processing program was also written to realize the detection area selection, RGB (red, green, blue) value reading and detection signal calculation. With the integrated device, a biomarker, human immunodeficiency virus (HIV) p24 antigen in human serum was detected down to 20 pg/ml, much better than that with microchannels. The proposed microchip and the imaging system can be a promising POCT or on-site detection tool based on colorimetric detection.

Hierarchical self-assembly of nanoparticles (NPs) mediated by macrocyclic molecules, cucurbiturils (CBs), provides a facile method to fabricate surface-enhanced Raman spectroscopy (SERS) sensors for potential applications in biosensing and environmental monitoring. In contrast to conventional techniques for wastewater-based epidemiology (WBE), CB-NP SERS sensors offer great opportunities for on-site quantification of trace chemical and biological markers due to its high sensitivity, selectivity, reproducibility, multiplexing capability and tolerance against contamination. The working principles of the CB-Au NP nanocomposites including fabrication, sensing mechanisms and structure-property relationships are explained while the design guidelines and selected examples of CB-Au NP SERS sensors are discussed. The review concludes by highlighting recent advances in this area and exploring opportunities in the context of WBE.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.

Membrane proteins perform a vast range of vital biological functions and are the gatekeepers for exchange of information and matter between the intracellular and extracellular environment. However, membrane protein interactions can be challenging to characterise in a quantitative manner due to the low solubility and large size of the membrane protein complex with associated lipid or detergent molecules. Here, we show that measurements of the changes in charge and diffusivity on the micron scale allow for non-disruptive studies of membrane protein interactions in solution. The approach presented here uses measurements of key physical properties of membrane proteins and their ligands to characterise the binding equilibrium parameters. We demonstrate this approach for human aquaporins (AQPs), key membrane proteins in the regulation of water homeostasis in cells. We perform quantitative measurements to characterise the interactions between two full-length AQP isoforms and the regulatory protein, calmodulin (CaM), and show that CaM selectively binds AQP0. Through direct measurements of the diffusivity and mobility in an external electric field, the diffusion coefficients and electrophoretic mobilities are determined for the individual components and the resulting AQP0-CaM complex. Furthermore, we obtain directly the binding equilibrium parameters and effective charge of each component. These results open up a route towards the use of microfluidics as a general platform in protein science and open up new possibilities for the characterisation of membrane protein interactions in solution.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of novel method for protein identification in a microfluidic format.

Protein identification and profiling is critical for the advancement of cell and molecular biology as well as medical diagnostics. Although mass spectrometry and protein microarrays are commonly used for protein identification, both methods require extensive experimental steps and long data analysis times. Here we present a microfluidic top down proteomics platform giving multidimensional read outs of the essential amino acids of proteins. We obtain hydrodynamic radius and fluorescence signals relating to the content of tryptophans, tyrosines and lysines of proteins using a combination of diffusional sizing of proteins, label-free detection and on-chip labelling of proteins with a latent fluorophore in the solution phase. We thereby achieve identification of proteins on a single microfluidic chip by separating and mapping proteins in multidimensional space based on their characteristic physical parameters. Our results have significant implications in the development of easy and rapid platforms to use for native protein identification in clinical and laboratory settings.

2 Optical detection has become a convenient and scalable approach to read out infor- 3 mation from micro(cid:13)uidic systems. For the study of many key biomolecules, however, 4 including peptides and proteins, which have low (cid:13)uorescence emission efficiencies at 5 visible wavelengths, this approach typically requires labelling of the species of interest with extrinsic (cid:13)uorophores to enhance the optical signal obtained { a process which can be time-consuming, requires puri(cid:12)cation steps, and has the propensity to perturb the behaviour of the systems under study due to interactions between the labels and the analyte molecules. As such, the exploitation of the intrinsic (cid:13)uorescence of protein molecules in the UV range of the electromagnetic spectrum is an attractive path to allow the study of unlabelled proteins. However, direct visualisation using 280 nm excitation in micro(cid:13)uidic devices has to date commonly required the use of coherent sources with frequency multipliers and devices fabricated out of materials that are incompatible with soft-lithography techniques. Here, we have developed a simple, robust and cost-effective 280 nm LED platform that allows real-time visualisation of intrinsic (cid:13)uorescence from both unlabelled proteins and protein complexes in poly- dimethylsiloxane micro(cid:13)uidic channels fabricated through soft-lithography. Using this platform, we demonstrate intrinsic (cid:13)uorescence visualisation of proteins at nanomo- lar concentrations on chip, and combine visualisation with micron-scale diffusional 20 sizing to measure the hydrodynamic radii of individual proteins and protein complexes 21 under their native conditions in solution in a label-free manner. with molecular mass of 20.1 kDa. Our results show that the measured hydrodynamic radius 222 for this system is signi(cid:12)cantly larger than that expected from a scaling relationship between 223 molecular mass and radius, ((cid:12)gure 5). These (cid:12)ndings obtained under fully native conditions 224 and for unlabelled molecules, indicates that the monomeric protein is forming complexes under these conditions. Sizing of self-assembled protein-structures can be challenging with 226