Cardiomyopathies are often characterized by significant fibrotic remodelling of the heart, marked by an abnormal accumulation of collagen type I. Label free Raman spectroscopy, a non-invasive diagnostic technique, holds promise for monitoring biochemical changes throughout the initiation and progression of different diseases, including cardiomyopathies. This study demonstrates the effectiveness of 70% glycerol as a hyperosmotic immersion liquid for in-depth controlling the optical properties of ex vivo myocardium tissue during deep-UV Raman spectroscopy with 244 nm excitation. The results revealed a considerable enhancement in the intensities of Raman peak, particularly the amide I region after glycerol treatment. This occurred across all depths (0−120 µm) and glycerol treatment durations (30 and 60 min). A noticeable enhancement of the Raman peak at 1647 cm⁻¹ was also observed that is attributable to structural transformations of the collagen due to the dehydration induced by glycerol. This finding suggest that deep-UV Raman can be employed as a specific probe of the collagen environment. As the amide I region reflects structural changes in collagen type I, these findings propose the potential of deep-UV Raman spectroscopy in combination with glycerol as optical clearing agent for monitoring collagen modifications.

All-perovskite tandem solar cells are a promising photovoltaic technology, but their efficiency is strongly limited by the tunnel junction. The tunnel junction enables carrier tunneling and recombination, which depend on the interfacial band alignment. Through quantitative simulations using Silvaco Technology Computer Aided Design (TCAD), we find that hole tunneling is intrinsically more difficult than electron tunneling in the tunnel junction. Efficient tunnel junctions require minimizing the barrier for holes while maintaining a moderate barrier for electrons to balance tunneling. For the SnO₂/metal/PEDOT:PSS tunnel junction in all-perovskite tandem solar cells, tuning the metal work function achieves balanced electron and hole tunneling, reduces junction resistance, and directly enhances performance of tandem solar cells. This work provides quantitative design rules for tunnel junction optimization, offering a clear pathway toward high-performance all-perovskite tandem solar cells.

Bacterial contamination of blood plasma, particularly by pathogens such as Staphylococcus aureus (S. aureus), including antibiotic resistant strains (e.g., MRSA), remains a critical challenge in transfusion medicine. Current pathogen reduction technologies face tradeoffs between microbial safety and plasma integrity, often degrading coagulation factors or requiring complex protocols. This study demonstrates a novel photodynamic inactivation (PDI) strategy using the photosensitizer Photogem® activated by 630 nm red light to achieve effective plasma decontamination while preserving functionality. Through systematic optimization of photosensitizer concentration (25–50 μg/mL) and light doses (15–60 J/cm²), we achieved a 3-log CFU/mL reduction of S. aureus in artificial plasma at 50 μg/mL with 60 J/cm² irradiation, matching FDA sterilization thresholds for blood products. Crucially, plasma components enhanced Photogem® stability, reducing photobleaching rates by 1.5–2.5 × compared to PBS (decay constants: 0.025–0.07 min⁻¹ vs. 0.045–0.1 min⁻¹) through protein mediated molecular interactions. Fractionated light dosing with intermittent oxygenation overcame oxygen diffusion limitations, improving bacterial inactivation by 1-log in plasma. Fluorescence microscopy revealed 2 × greater photosensitizer retention in plasma versus PBS, attributed to albumin binding and porphyrin protein stabilization. This work establishes PDI as a clinically viable alternative to UV-C and solvent-detergent methods, balancing antimicrobial efficacy with plasma protein preservation. Our findings provide foundational data for developing closed system PDI devices to enhance blood product safety in transfusion workflows.

Optical neural networks (ONNs) hold great promise for low-latency, energy-efficient inference. However, the absence of a fully real-valued end-to-end ONN, in which the inputs, weight matrices, and nonlinear activations are all represented in the real-number domain and can be optically cascaded, remains a key bottleneck. Existing approaches either rely on electrical post-processing of photodetector outputs to extend the number field in the linear layers, which breaks optical cascadability, or employ photodiode–driven micro-ring modulators (MRMs) to implement nonlinearities, constraining subsequent-layer inputs to the nonnegative domain and thereby limiting network expressivity and architectural flexibility. Here, we employ two MRMs biased at different resonance wavelengths to achieve real-valued optical encoding, together with a dual-MRM activation element driven by the differential photocurrent of photodiodes, which provides optically cascadable real-valued nonlinear activation. Combined with a real-valued Mach–Zehnder interferometer mesh for matrix computation, this architecture realizes a fully real-valued end-to-end ONN. We experimentally demonstrate a tanh-like nonlinear activation function and validate it on an iris classification task, achieving an accuracy of 98%. We further model the generator of a generative adversarial network based on this structure, in which the nonlinear activation is based on the experimentally measured nonlinear transfer curve. The generator can use natural optical noise as its input, thereby eliminating electro-optic conversion and digital-to-analog conversion at the input stage. With the above merits, the proposed ONN achieves successful optical-to-optical on-chip image generation, validating the superiority of optical computing.

Photodynamic therapy (PDT) has emerged as a clinically approved, non-invasive cancer treatment that induces tumor cell death through the intracellular generation of reactive oxygen species (ROS) upon exposure to non-ionizing radiation. Its effectiveness relies on the synergistic action of a photosensitizer (PS), light, and molecular oxygen, with the therapeutic response shaped by the PS’s selective accumulation in tumor-associated organelles such as mitochondria and lysosomes. Despite its clinical utility, conventional PDT faces significant challenges, including limited specificity, poor bioavailability. To address these limitations, recent advances in nanotechnology, particularly liposomal drug delivery systems, have demonstrated significant potential in enhancing the efficiency and selectivity of PDT. Liposomes, with their amphiphilic nature and biocompatibility, enable controlled PS release, enhanced tumor targeting, and reduced systemic toxicity. Furthermore, functionalization with ligands and integration of imaging agents have led to the development of multifunctional liposomal nanocarriers capable of simultaneous therapy and diagnosis (theranostics). This review discusses the evolving trends in liposome-based nanomedicine for PDT, including the incorporation of green nanotechnology approaches that utilize biologically derived agents to synthesize eco-friendly nanoparticles with improved photochemical performance. The review also emphasizes the role of surface modification strategies to boost cancer cell specificity, highlighting recent developments aimed at improving the clinical translation of liposome-based PDT systems for more precise and effective cancer treatment.

Low-frequency electric field sensors are essential for applications in geophysics, electrical engineering, aerospace, and medical technology. However, conventional technologies often suffer from intrinsic trade-offs among traceability, multidimensional vector detection, and miniaturization, which significantly hinder their scalability and deployment in compact platforms. To address these challenges, we propose a vector-resolved quasi-static electric field sensor based on a Rydberg dipolar chain, where the external field reorients the atomic quantization axis and thereby modulates the angle-dependent dipolar exchange interaction. Using a unified framework combining time-domain propagation, Ramsey-mode spectroscopy, and end-to-end Green’s-function analysis, we identify three complementary observables—arrival time, eigenmode frequency shifts, and transmission fringes—that encode both the amplitude and direction of the applied field. The approach operates at micrometer scales compatible with optical-tweezer arrays, offers tunable sensitivity near the magic angle, and provides multi-channel readout within a single platform. Our results establish a compact and experimentally feasible route toward high-resolution, vector-sensitive low-frequency electrometry with the potential for quantum-enhanced performance.