Flexible wearable materials have frequently been used in drug delivery, healthcare monitoring, and wearable sensors for decades. As a novel type of artificially designed functional material, photonic crystals (PCs) are sensitive to the changes in the external environment and stimuli signals. However, the rigidity of the PCs limits their application in the field of biometric and optical sensors. This study selects silk fibroin (SF) and poly-N-isopropylacrylamide (PNIPAM) as principal components to prepare the hydrogel with the physical crosslinking agent lithium silicate (LMSH) and is then integrated with PCs to obtain the SF-PNIPAM dual-crosslinked nanocomposite for temperature and strain sensing. The structural colors of the PCs change from blue to orange-red by the variation in temperature or strain. The visual temperature-sensing and adhesion properties enable the SF-PNIPAM dual-crosslinked nanocomposite to be directly attached to the skin in order to monitor the real-time dynamic of human temperature. Based on its excellent optical properties and biocompatibility, the SF-PNIPAM dual-crosslinked nanocomposite can be applied to the field of visual biosensing, wearable display devices, and wound dressing materials.

Photoelectric dual-mode sensors, which respond to strain signal through photoelectric dual-signals, hold great promise as wearable sensors in human motion monitoring. In this work, a photoelectric dual-mode sensor based on photonic crystals hydrogel was developed for human joint motion detection. The optical signal of the sensor originated from the structural color of photonic crystals, which was achieved by tuning the polymethyl methacrylate (PMMA) microspheres diameter. The reflective peak of the sensor, based on 250 nm PMMA PCs, shifted from 623 nm to 492 nm with 100% strain. Graphene was employed to enhance the electrical signal of the sensor, resulting in a conductivity increase from 9.33 × 10⁻⁴ S/m to 2 × 10⁻³ S/m with an increase in graphene from 0 to 8 mg·mL⁻¹. Concurrently, the resistance of the hydrogel with 8 mg·mL⁻¹ graphene increased from 160 kΩ to 485 kΩ with a gauge factor (GF) = 0.02 under 100% strain, while maintaining a good cyclic stability. The results of the sensing and monitoring of finger joint bending revealed a significant shift in the reflective peak of the photoelectric dual-mode sensor from 624 nm to 526 nm. Additionally, its resistance change rate was measured at 1.72 with a 90° bending angle. These findings suggest that the photoelectric dual-mode sensor had the capability to detect the strain signal with photoelectric dual-mode signals, and indicates its great potential for the sensing and monitoring of joint motion.

Theoretical calculations and numerical simulations play a crucial role in analyzing material properties and devising effective research strategies. In this study, the photonic band gap (PBG) of polymethyl methacrylate (PMMA) and polystyrene (PS) photonic crystals was successfully predicted using theoretical calculations and numerical simulations. The agreement between the predicted results and the actual reflection peaks reached an impressive level of 99%. Utilizing SEM images, the prediction of reflection peaks in acrylamide (AM)—based photonic hydrogels was conducted using theoretical formulas and Rsoft 2019–Bandsolve software v2019.09. The relationship between the actual reflection peaks and compressive strains in AM-based photonic hydrogels featuring 251 nm PMMA PCs exhibited a remarkable similarity of over 96% with the theoretical and simulated results. In conclusion, an exploration was conducted into the relationship between reflection peaks and compressive strains for AM-based 270 nm PMMA photonic hydrogels, allowing the prediction of the actual reflection peaks under compression. The consistency observed between theoretical/simulated reflection peaks and actual reflection peaks validates the efficacy of this approach in evaluating the optical properties of photonic materials and predicting their responsive effects. This method offers a straightforward and expeditious solution for the design and fabrication of photonic materials.

Foodborne diseases impact 600 million people annually, causing 420,000 deaths and over $110 billion in economic losses. Conventional pathogen detection methods take 3-7 days and lack sensitivity for low-abundance pathogens and antimicrobial resistance (AMR) genes. This review evaluates nanopore sequencing's potential in food microbiology, analyzing real-time pathogen detection, AMR surveillance, and performance versus traditional methods. A systematic review of 80 studies (2015–2025) revealed nanopore sequencing reduces detection time to 90% AMR gene sensitivity, at $50–200 per sample versus $300–800 for traditional whole-genome sequencing. It enables multiplex pathogen detection, viable but non-culturable organism identification, and real-time AMR profiling. However, challenges include error rates of 8–15% in homopolymeric regions and the need for bioinformatics expertise. Addressing challenges, including error rates of 8–15% in homopolymeric regions and the need for bioinformatics expertise, requires strategic mitigation approaches. Despite limitations, advances in base-calling and workflows make nanopore sequencing a transformative tool for next-generation food safety surveillance.

The on-site and high-throughput screening of explosives has become increasingly significant in counter-terrorism and national security. We propose a simple and low-cost method for the screening of several explosives assisted by molecularly imprinted colloidal arrays within the 24-well plate. The infiltration and incorporation of various explosives in the interstitials of imprinted colloidal photonic crystals (CPC) result in red-shift and amplitude reduction of optical stop bands of the CPC. A certain linear relationship between the concentration of explosives and the Bragg’s diffraction shifts has been observed for a variety of explosives involving 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20, LOD 0.085 mM), hexahydro-1,3,5-trinitro-1,3,5-triazine(RDX, LOD 0.102 mM), 2,4,6-trinitrotoluene(TNT, LOD 0.286 mM), 2,4-dinitrotoluene(2,4-DNT, LOD 0.089 mM), 2,6-dinitrotoluene(2, 6-DNT, LOD 0.238 mM), and 1,3,5-trinitrobenzene (TNB, LOD 0.184 mM). Significantly, pattern recognition and Principal Component Analysis (PCA) were utilized to discriminate the different explosives. The proposed method was successfully applied to the high-throughput screening of synthetic explosive solutions.