Bacterial infection theranostics combining antibacterial therapy and real-time diagnosis can effectively advance the healing process. Near-infrared (NIR) light has been widely utilized for antibacterial photothermal therapy (PTT) and visible light can provide visual cues for the status of treatment, whereas the lack of modulating light propagation hinders the development of high-performance light-based infection theranostics. Here, inspired by the hierarchical micro/nano-structures of panther chameleon skin composed of deep- and superficial-iridophores responsible for regulating NIR and visible light propagation, respectively, a photonic crystal hydrogel is developed for enhanced antibacterial PTT and colorimetric monitoring of pH and treatment temperature. The deep layer composed of large-sized particles in the hyaluronic acid methacryloyl-polyacrylamide hydrogel matrix exhibits a photonic bandgap overlapping NIR light, acting as a universal platform for boosting the photothermal conversion efficiency (PCE) of embedded photothermal agents. As typical examples, 1.75-, 1.80-, and 1.94-fold increases in PCEs are achieved for embedded carbon black, carbon nanotubes, and MXenes, respectively. The superficial layer consisting of small-sized particles and a poly(2-(dimethylamino)ethyl methacrylate) hydrogel matrix is responsible for visible light modulation, exhibiting rapid, high-sensitivity, and broad-range color variations at different pH/temperatures. Benefiting from these light modulation capabilities, high-efficacy and multifunctional bacterial infection theranostics are realized, synergistically facilitating the healing of infected wounds.

Proteins orchestrate nearly all cellular processes and serve as key biomarkers and therapeutic targets. Conventional detection bioassays are confined in centralized laboratories, dependent on bulky instruments or labeling workflows. Currently, they are limited to merely read out the concentration of proteins, leaving molecular details such as layer thickness and orientation inaccessible, which are critical for functional assessment. Here, we present a Mie-resonant nanosensor that transduces biomolecular binding events into vivid colorimetric changes through high-order quadrupole modes in the visible spectrum, unprecedently extending colorimetric sensing to the biomolecular scale. Coherent quadrupole interference enhances backward scattering enabling optical readout of protein layers as thin as 1.8 nm along with recognizing protein orientation, termed as the visualized Mie-resonance sensing (VIMS). Both quality control of antibody functionalization and quantitative detection of antigens can be achieved via VIMS, demonstrating a 0.4 pg/mL detection limit of cardiac troponin T (cTnT) within 20 minutes. Integrated with a smartphone-compatible point-of-care platform, the assay reliably diagnoses acute myocardial infarction (AUC u0026gt; 0.95) from serum, saliva and urine (N = 220), and identifies elevated baseline cTnT in high-stress populations. This work bridges nanophotonic field confinement with biomolecular structural resolution, enabling label-free, portable, and quantitative molecular-scale optical sensing for decentralized precision diagnostics.

Green printing has emerged as a powerful additive manufacturing strategy for fabricating functional materials and devices over large areas, while reducing material consumption, processing steps, production costs and chemical waste. By precisely controlling droplet movement, fluid transport, and interfacial assembly, printing enables the direct patterning of highly integrated structures on various substrates. Over the past five years, rapid advances in functional ink design, printing processes, device integration, and artificial intelligence (AI) have accelerated the transition of printed materials and technologies from laboratory demonstrations towards industrial manufacturing. Nevertheless, their broader deployment remains limited by persistent trade-offs between resolution, throughput, scalability and reproducibility. In this review, we first summarize recent developments in inkjet printing, template-guided printing, 3D printing, transfer printing, roll-to-roll manufacturing and other emerging printing techniques, with emphasis on improving material utilization, simplifying processing procedures, and achieving large-scale production. We then discuss printable functional materials, including quantum dots, nanomaterials, perovskites, polymers, and their composites, highlighting the roles of material composition, rheology, and stability in determining their final functionalities. On this basis, we introduce representative applications in electronics, photonics, biomedical engineering, microfluidics and soft robotics. We further examine the effect of AI on green printing in various aspects such as ink formulations, process optimization, structural design, and closed-loop control. Finally, we discuss the challenges and future prospects for achieving green and sustainable printing in practical use.

Brain gliomas' variable growth patterns and locations hinder complete surgical removal, while existing clinical imaging/histological methods fail to guide intraoperative resection. To address this, we developed blue-green dual-color probes (perovskite quantum dots conjugated to antibodies) targeting glioma cells and IDH1-mutant simultaneously. These enable rapid intraoperative in vitro pathological diagnosis, helping surgeons devise resection strategies for personalized precise treatment. Tested on 56 clinical frozen sections, the combined detection using dual probes can be completed within 30 min with 91% accuracy: the glioma-targeting probe identifies tumor boundaries, while the IDH1-targeting one distinguishes subtypes for optimized margin delineation. We established a personalized resection strategy guided by these results; the dual-target probes show great potential to assist neurosurgeons in precise glioma resection.

Traumatic brain injury (TBI) triage and monitoring demand rapid, sensitive, and deployable biomarker assays. We present an AI-integrated nanophotonic biosensor enabling ultrasensitive multiplexed quantification of S100B, GFAP, and UCH-L1 across diverse human biofluids. The sensing substrate consists of polystyrene single chains formed via liquid-confinement self-assembly and functionalized with antibodies. Selenium nanoparticles are pre-incubated with specimens to form SeNP-antigen complexes that co-localize on the chains, generating high-contrast elastic scattering. A standardized four-zone layout employs hydrophilic/hydrophobic patterning to passively isolate reaction domains without physical barriers. A deep learning pipeline facilitates device-agnostic quantification from images captured using various microscopy systems, including professional setups and smartphone-based adaptations. Validation with 195 clinical specimens from 75 individuals (TBI patients and controls) spanning serum, urine, saliva, and cerebrospinal fluid demonstrated a detection limit of 1 pg mL-1 and strong agreement with ELISA (R2 > 0.93) for all biomarkers, with consistent performance across imaging modalities. The workflow is completed in ∼30 min and requires only a smartphone in portable modes, demonstrating a proof-of-concept for point-of-care neurotrauma diagnostics and highlighting a potential pathway toward AI-assisted decentralized TBI management.

Traumatic brain injury (TBI) is a leading cause of global mortality and long-term disability, but its diagnosis remains challenging due to the limitations of conventional imaging and biomarker assays. S100B protein is a clinically validated biomarker for TBI, yet rapid and ultrasensitive detection in diverse biofluids is technically difficult. To address this, we developed a nanophotonic biosensor integrated with deep learning. This platform enables rapid, label-free, and ultrasensitive detection of S100B in human and animal biofluids. The biosensor uses a printed heterochain chip made of polystyrene and selenium nanoparticles, combined with red-channel image analysis and convolutional neural network classification. The system showed pg/mL-level sensitivity and high quantitative fidelity (R2= 0.9996) over six orders of magnitude. We validated a strong correlation with enzyme-linked immunosorbent assay (ELISA) in serum, urine, saliva, and cerebrospinal fluid samples from 30 human participants and 50 TBI model mice. The biosensor reliably stratified injury severity and detected S100B as early as 5 min post-injury. These findings indicate that the biosensor-AI platform is promising for early TBI diagnosis and severity assessment.

Secondary lymphedema (SLE) is one of the common postoperative complications in cancer patients. Without timely intervention, it may progress to fibrosis and even recurrent infections, significantly impairing patients' quality of life. Substantial individual variability in the latency period of limb swelling, coupled with the absence of reliable biomarkers, poses considerable challenges for early detection of SLE. Preliminary studies have suggested macrophage inflammatory protein-1(3 (MIP-1(3) as a potential predictive marker for lymphedema; however, its clinical translation has been hampered by the insufficient sensitivity of conventional detection techniques. To address the challenge, we developed a photonic crystal-based biochip with enhanced detection capabilities. After detecting 60 human plasma samples, the photonic crystal-based biochip achieved a sensitivity for plasma MIP-1(3 as low as 3.76 pg/mL, while revealing a positive correlation between MIP-1(3 concentration and the severity of limb edema in postoperative patients. This enables the determination of clinical thresholds and the correlation with SLE staging. A logistic regression model incorporating patients' clinical history demonstrated a predictive accuracy of 80.75% for lymphedema-associated limb edema. This study not only confirms the clinical utility of plasma MIP-1(3 as a SLE biomarker but also highlights the potential of portable photonic crystal microarray technology. The developed platform enables rapid, sensitive detection requiring merely a single droplet of blood, offering a promising novel strategy for the early prediction and potential staging of limb edema associated with SLE.

Background: Rapid and accurate identification of stroke subtype is critical for timely intervention, yet current diagnostic assays are limited by long turnaround times, dependency on centralized laboratories, and insufficient sensitivity in the ultra-early stage. Methods: We developed a nanophotonic heterochain biosensing platform integrated with deep learning-assisted image analysis for multiplex detection of S100B, GFAP, and UCH-L1 in serum, urine, and saliva. The system consists of antibody-functionalized PS@Se heterochain biosensor, portable smartphone-based imaging, and a cloud-enabled analysis pipeline. Analytical validation was performed using recombinant protein standards, followed by clinical evaluation in ischemic stroke, intracerebral hemorrhage, and healthy controls. Results: The biosensor achieved picogram-level sensitivity with strong linearity (overall R2 = 0.9583), high recovery (104.7%), and acceptable reproducibility (CV = 24.4%). Clinical validation demonstrated significant elevations of biomarkers in patient groups compared to controls, with distinct profiles between ischemic and hemorrhagic stroke. Integration of biomarker panels using logistic regression improved diagnostic performance over individual assays, enabling accurate discrimination of stroke subtypes within a clinically actionable window. Conclusion: This portable, AI-integrated biosensing system enables ultra-early, multiplexed, and quantitative detection of brain injury biomarkers, with potential to transform point-of-care stroke diagnostics in both clinical and field settings.

Large-area and high-spatial-resolution photodetectors with the ability of light direction recognition have promising application prospects in smart sensors, human-machine interaction, the Internet of Things, and other fields. However, current photodetectors often fail to achieve accurate direction recognition of light and require a trade-off between high spatial resolution and device size. Here, we show a printed microscale perovskite dual-line structure designed for resonant, directionally selective absorption, which creates differentiated photocurrents under visible light from different directions. After investigating the change of reflected light from the eyeball, a single dual-line microstructure can be integrated as a wearable photodetector for monitoring eye movement abnormalities. The frequency and amplitude of eye movement can be recorded for early warning of neurological disorders and monitoring nervous system diseases. This strategy provides a new approach for creating high-performance optoelectronic devices using printed photonic resonant structures, which extends the application of optoelectronic devices in brain science.

Point-of-care testing (POCT) aims to deliver laboratory-based analytical platforms to resource-limited settings, yet its development is often hindered by cumbersome sample preparation and complex detection workflows. Here, we report an optical biosensing chip that integrates the fluorescence enhancement effect of photonic crystal arrays with the microfluidic automation for rapid and highly sensitive quantification of alpha-fetoprotein (AFP) in blood. The pre-analytical process for serum separation is also simplified by replacing the conventional centrifugation with an on-chip membrane filtration step. After the optimization of the antibody modification parameters on the PC arrays and the hydrodynamic performance, the optical biosensor exhibits a detection limit as low as 0.1 ng mL-1 for AFP and a broad linear range from 1 to 1600 ng mL-1via one-droplet blood. After conducting a double-blinded comparison with the clinical gold standard ELISA using 70 clinical samples, high accuracy and agreement were achieved with a Pearson correlation coefficient of R2 = 0.981, and a mean bias of -5.15 ng mL-1via Bland-Altman analysis. The optical biosensor integrates the high sensitivity of photonic crystals, the automation of microfluidics, and the simplicity of membrane filtration for the development of next-generation on-site diagnostic devices, which offers a practical solution to the key challenges of simplified, point-of-care diagnosis of hepatic disease.

Organoids are 3D artificial miniature organs composed of a cluster of self-renewing and self-organizing cells in vitro, which mimic the functions of real organs. Nanotechnologies, including the preparation of nanomaterials and the fabrication of micro/nanostructures, have been proven to promote cell proliferation, guide cell differentiation, and regulate cell self-organization, showing great promise in engineering organoids. In this Perspective, different types of nanocomposite hydrogels for organoid culture are summarized, the effects of micro/nanostructures on organoid growth and development are discussed, and 3D bioprinting technologies for constructing organoid models are introduced.

Hotspot engineering of the surface-enhanced Raman scattering (SERS) system is developed to achieve large enhancement factors, which are limited by small Raman scattering cross-sections on low-dimensional structures. Hotspots between the nanogap are difficult to control as discrete occurrences, which are a critical challenge for high-sensitivity detection. In this work, a hotspot engineering strategy is proposed by introducing 3D hierarchical micro/nanostructures and spatial distribution manipulation. Based on the template-induced printing strategy, the polystyrene microspheres covered by a quasi-periodically patterned silver nanoparticles (AgNPs) lattice can be assembled into clusters with precise configurations. The surface curvature of the AgNPs lattice and spatial arrangement of the clusters stimulate electromagnetic wave localization and regulate electromagnetic resonance, creating high-density and high-intensity hotspots. Through experiments and theoretical calculations, 3D clusters with a triangular configuration are identified for highly improved Raman response and achieve an ultralow detection limit (LOD) of 10-20 mol L-1 for R6G, which is the currently lowest detection limit. The 3D platform also detects inflammatory markers of procalcitonin and interleukin-6 with LODs of 1.84 fg mL-1 and 1.75 pg mL-1 without labels, demonstrating the potential for ultrasensitive bio-detections. The approach will offer new strategies towards 3D SERS platforms design for simple and ultrasensitive disease diagnosis.

There is an urgent requirement to improve accessibility to diagnostic tools in remote areas. This requires assays that are easy to use, are cost-effective and produce rapid results. Important public health applications include early disease diagnosis, real-time monitoring, epidemic control and medical cost control. This protocol describes the fabrication of all-printed photonic crystal (PC) biochips for point-of-care testing of biomarkers. The photonic crystal material is prepared by the self-assembly of latex nanospheres that are printed onto a polyethylene terephthalate substrate. Photonic crystals composed of latex nanospheres of different sizes enhance the fluorescent signal emitted at different wavelengths, resulting in remarkably higher detection sensitivity. PC microarrays enable mass-printed preparation (up to 2,700 pieces can be printed per hour by one printer), and each microarray can be stored for a long time (>6 months) after heating. Biomarker specificity is achieved by the biofunctionalization of the nanospheres, for example, attaching capture antibodies. The detection involves the use of a fluorescently labeled detection antibody and a simple point-of-care detection device. This universal approach can be applied to the detection of many biomarkers, and the simultaneous detection of multiple biomarkers is also possible. Here we demonstrate describing how to prepare a chip that can be used to detect three inflammatory biomarkers in 10 min at low sample volumes at a cost of less than 3 CNY (~US$0.41) per PC codetection biochip. The biofunctionalization process including capture antibody coupling and blocking takes 3–4 h, and the detection process takes 20 min. There is an ongoing need for diagnostic tools that can be used in remote areas. This protocol describes the preparation of a chip-based assay with a simple detector for the analysis of biomarkers in biofluids.

Diabetic kidney disease (DKD) remains one of the most serious complications of type 2 diabetes, significantly impacting patients' morbidity and mortality. Microalbuminuria (mALB), a clinically validated biomarker, plays a key indicator for both early diagnosis and predicting the progression of DKD. However, existing point-of-care testing methods for albumin detection often suffer from limited sensitivity and operational complexity. To overcome these challenges, we developed and evaluated a fully printed, photonic crystal-integrated microarray-assisted point-of-care platform specifically designed for the rapid and precise detection of mALB. The system employs polymer latex microspheres, selected for their optical compatibility with detection fluorophores, as signal-enhancing carriers. By exploiting the photonic bandgap and photon localization effects inherent in photonic crystal structures, the platform significantly amplifies fluorescence signals in immunoassays, thereby substantially enhancing detection sensitivity and resolution. A detection method based on a double-antibody sandwich immunoassay was employed, enabling specific antigen-antibody binding and accurate quantification. Validation using clinical urine samples demonstrated the diagnostic efficiency of the platform. The full analysis can be executed in under 10 min, demonstrating a robust linear association between the fluorescence level and the mALB content.. The platform achieved a detection limit of 19.5 pg mL-1 and an accuracy of up to 92.9%. Further validation with 70 clinical urine samples confirmed that the platform exhibited a good diagnostic performance, demonstrating high sensitivity, specificity, and operational stability. In summary, this photonic crystal microarray-based point-of-care system offers a sensitive, accurate, and time-efficient solution for early DKD diagnosis and long-term disease monitoring. Its convenience and portability render it ideal for frequent assessments and long-term patient care, possibly enhancing results and decreasing medical expenses.

Gyms are indoor environments in which many people perform physical exercise and could potentially increase the risks of bacterial contamination and dissemination. Staphylococcus aureus (S. aureus) is one of the most prevalent bacteria in community-acquired infections; thus, the rapid detection and continuous monitoring of S. aureus are crucial for evaluating the hygienic status of gym environments. This work describes the fabrication of a nanochain-based biosensor for S. aureus detection using carboxyl-modified polystyrene (PS) nanoparticles functionalized with a specific antibody. When target bacteria bind to the nanochains, they yield distinct color changes which support the directly visualizable analysis of optical images, recorded using optical microscopy or even a smart mobile phone. In addition to high portability, this biosensor is also capable of the quantification and continuous monitoring of the bacterial load in a gym environment over a broad linear range (100 CFU/mL~105 CFU/mL), with a detection limit of 1 CFU/mL. In summary, this study validated the applicability of the biosensors for the rapid detection and real-time monitoring of gym environmental pathogens.

Respiratory infections are the major cause of death from infectious diseases worldwide, which impose a heavy burden on public healthcare. Many respiratory viruses induce indistinguishable clinical symptoms, making accurate detection and effective surveillance challenging. Here, a polarization‐sensitive multiplex biochip is presented utilizing self‐assembled anisotropic nanochains for simultaneous colorimetric detection of multiple pathogens in human nasal, throat, and serum samples. The biochip features a patterned wettability surface, wherein the hydrophobic pattern facilitates precise sample segmentation, blocking the detection interferences. The hydrophilic areas can be utilized not only to print functionalized nanochains for selective identifications of different respiratory viruses, but also to support target preconcentration through the coffee‐ring phenomenon, enhancing the efficiency of virus detection. Furthermore, strong light confinement is shown near the nanochain surface by tailoring incident polarization, which enlarges colorimetric response sensitivity to virus loading. Thus, the simultaneous quantification of various respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), influenza A virus, influenza B virus, and adenovirus, is achieved with a detection limit of 10 PFU mL −1 . In clinical tests, influenza patients are successfully distinguished from other volunteers with an accuracy of 96.2%. This method eliminates the need for extrinsic labels or preamplification, which is expected to expand at‐home diagnostic tools for respiratory pathogens.

Perovskite quantum dots (PQDs) are promising materials for photonic and optoelectronic devices, relying on efficient and reliable patterning methods. However, the complex patterning process and poor stability of PQDs restrict their practical applications. Here, a patterning-induced encapsulation strategy (PIE-PQDs) is demonstrated for directly patterning PQDs into a thin polystyrene (PS) film. The prepared PQDs@PS composite film displays excellent air stability (30d, 92%), UV resistance (30d, 85%), and water resistance (30d,88%). Notably, even in harsh environments such as acid/alkali/alcohol aqueous solution, the composite film still preserves high luminescence. A binary solvent engineering strategy is induced to precisely control the distribution of PQDs inside the polymer film, resulting in morphologically controllable PQDs@PS microstructures for optical information encryption. Patterned PQDs@PS composite film can be compatible with diverse substrates including silicon, glass, paper, and plastic with the feature of optical information encryption. This method shows a universal in situ protection approach for patterning and integrating PQDs on flexible substrates, offering significant potential for display, optical data storage, information encryption, and anti-counterfeiting.

More than 70% of human information comes from vision. The eye is one of the most attractive sensing sites to collect biological parameters. However, it is urgent to develop a cost-effective and easy-to-use approach to monitor eyeball information in a minimally invasive way instead of current smart contact lenses or camera-based eyeglasses. Here, the biomimetic mineralization strategy is developed to prepare large-grained perovskite film on the glass with prepared ITO electrodes, which displays the on-off ratio close to 300 times at 500 Lux light intensity, and the responsiveness reaches 22.09 A W-1. The smart eyeglasses composed of perovskite-based photodetectors can directly convert the visual stimuli from the reflective light of eyeballs into electrical signals in all light circumstances. After scaling up the pretraining data and the model size, the smart eyeglasses achieve the noncontact monitoring of the eyeball movement with the recognition angle of 5°, which can be used to unobtrusively drive the model car with great freedom. The smart eyeglasses based on the perovskite photodetectors provide cost-effective approaches for monitoring eyeball movements, which will show great potential in the applications of man-machine control, augmented reality, individual healthcare, etc.

This study aimed to construct a perovskite quantum dot probe targeting isocitrate dehydrogenase 1 (IDH1) mutation. The probe is intended to enable rapid in vitro pathological diagnosis of IDH1-mutant glioma, guide surgeons in formulating scientific and rational resection strategies during surgery, and facilitate precise individualized intraoperative treatment for glioma patients. Via surface ligand engineering, a perovskite nanocrystal (PNC) probe modified with bromobutyric acid (BBA) was developed in this study. This probe enhances water/oxygen stability and addresses the limitation that restricts the application of perovskite quantum dots as fluorescent probes. The probe was used to stain intraoperative tumor frozen sections to verify its specific recognition capability for IDH1-mutant glioma. Verification using 50 glioma frozen sections demonstrated that the probe could accomplish rapid pathological detection within 30 min (with a 5-minute incubation period). Fluorescence imaging showed specific green fluorescence in IDH1-mutant glioma. When compared with genetic testing results, the probe exhibited the following detection performance: sensitivity of 100

Colloidal crystal engineering enables the precise construction of structures with remarkable properties. However, the flexible and synergistic regulation of multiple properties of colloidal crystals remains a significant challenge. Here, we inspire from Brazilian opals to self-assemble polymer nanoparticles in the gaps of a single-layer opal substrate to fabricate large-scale binary colloidal crystals (BCCs). These BCCs have well-defined sizes, compositions, and dimensions, of which the crystallization process is finely controlled by the Marangoni flow. Notably, we find a critical size for the simultaneous and independent regulation of their lattice resonance wavelength and intensity, forming a full-color palette. Moreover, these BCCs as optical coatings allow for high-contrast imaging of microbials, benefiting from strong spatial confinement. Compared to glass in clinical smearing, they have an order of magnitude improvement in chromatism without dyeing. This work demonstrates that BCCs hold great potential in creating multifunctional devices for various applications including information display, biological detection, and optical imaging.