This abstract presents hydrogel-based biofabrication approaches for creating biomimetic tissue models and organ-on-chip systems. By combining tunable hydrogels, 3D bioprinting, and microfluidics, these platforms recreate physiologically relevant microenvironments that support complex cell interactions, disease modeling, drug testing, and mechanistic studies of tissue function in health and disease.

This lecture will present organ‑on‑chip platforms with integrated optical sensors to enable real‑time, label‑free monitoring of tissue function and response to treatment. We will show how combining engineered 3D tissues with photonic sensing enables sensitive readouts of metabolic activity and inflammation. Examples in metabolic, rare, and aging‑related disease models will illustrate how these technologies provide more predictive preclinical data and support the evaluation of potential therapeutic strategies.

Subjects with diabetes, stroke, or spinal cord injury miss independence necessary for everyday activities. Advances in nervous system interfacing present a promising venue for assistance and rehabilitation of these individuals. Despite a wide range of possibilities for human-machine interfacing, the nature of the optimal interaction remains poorly understood. Knowledge gained from in-silico modelling of targeted neural structures can inform an optimized design of such interfacing, therefore we develop the exact models of different nerves, enabling for AI-based personalized treatments. We have pioneered several human-machine systems that translates artificial sensors' read-outs into "language" understandable by the nervous system. We engineered wearable neurorobotic combining electrical neurostimulation with portable exoskeletons. In a clinical study with patients with spinal cord or brain injury, the technology supported finger dexterity, tactile perception, and grasping control. We also developed the "smart orthosis" for diabetics, that "speaks" to their residual healthy nerves while diminishing pain. These studies present a keystone for the development of the future personalized neurotechnologies, empowering people to reclaim autonomy and quality of life.

A variety of global health threats, and personalized medicine require at-home rapid tests and health monitoring which can be performed by the patient. Field effect transistors (FETs) configured as biosensors serve as a suitable platform, compatible with modern semiconductor manufacturing, for label-free rapid sensing by converting interactions between target analytes and surfaces into real-time electrical signals. Here, we introduce a scalable, CMOS compatible functionalization strategy, applicable to any FET material, permitting local chemical modification of individual nanoscale FETs on the same chip with different bioreceptors from antibodies to aptamers, at sub-20 nm resolution and 200 nm pitch, a distance comparable to the pitch of modern FETs array in CMOS chips.

Understanding charge transfer at material interfaces is a central issue in surface science and is critical for the development of next-generation functional devices. Triboelectrification, originating from interfacial contact and separation, provides a unique platform to study surface-dependent charge transfer while enabling energy-autonomous sensing. In this work, we present our recent advances in solid–liquid triboelectric nanosensors (SL-TENSs), with a particular focus on interfacial charge transfer mechanisms and surface chemistry regulation. By integrating triboelectrification with electrostatic induction, surface-engineered materials can convert interfacial physicochemical interactions into measurable electrical signals without external power sources. Unlike conventional solid-solid triboelectric systems, solid-liquid interfaces offer richer surface states, dynamic adsorption behaviors, and tunable electrical double-layer effects, making them highly suitable for probing surface-dependent phenomena. We demonstrate that selective adsorption or binding of target species, such as metal ions, small molecules, proteins, and microorganisms, modulates interfacial charge transfer characteristics, leading to distinct and quantifiable electrical responses. Systematic surface modification and material selection enable correlation between surface chemistry, interfacial charge density, and output signal evolution, establishing a clear sensing mechanism grounded in surface science. These results not only address key limitations of traditional triboelectric devices in stability and selectivity but also provide new insights into solid–liquid interfacial electrification. This study highlights the potential of SL-TENSs as both practical self-powered sensors and experimental platforms for investigating interfacial charge transfer and surface phenomena.

Join us at MicroTAS 2026 to discover how portable microfluidics brings lab-grade precision to everyday analytics. This talk showcases two groundbreaking technologies: an on-packaging smart sensor that tracks seafood freshness via biogenic amines, and a wearable device measuring personal PM 2.5 exposure in real time. See how material innovation and smart design are redefining decentralized sensing for human health!

Therapeutic drug monitoring can improve treatment outcomes, but current analytical methods are often slow and centralized. This talk presents integrated microfluidic SERS platforms that combine sample preparation and detection on-chip, enabling rapid drug measurements directly from blood. Recent advances, clinical applications, and the path toward commercialization will be discussed.

Photonic sensors based on dielectric nanostructures are enjoying much attention based on the drive toward personalized healthcare diagnostics and the desire for ubiquitous sensing. We will discuss the advantages of such sensors both for refractive index sensing, where we have demonstrated clinically relevant biomarker detection into the pg/ml regime, and for spectroscopy, where we have shown Raman scattering with enhancements higher than those observed with metallic nanostructures/SERS.