Biosensors & Biosensing Technology

Abstract: Scalable Breath Figure–Engineered Periodic Silver Nanostructures on Polymeric
Substrates for Ultrasensitive and Reusable SERS Biosensing

Surface-Enhanced Raman Scattering (SERS) has emerged as a promising next
generation biosensing technology due to its molecular specificity and ultrahigh sensitivity.
However, the practical translation of SERS into real-world biosensing platforms remains
constrained by expensive fabrication routes, limited scalability, and poor reusability of
conventional substrates. In this work, we report a scalable, low-cost, and reusable photonic
plasmonic biosensing platform fabricated via the breath figure (BF) self-assembly approach,
addressing key challenges in the development of future biosensors.
The BF method enables the formation of large-area, highly ordered periodic polymeric
architectures that act as templates for the controlled confinement of silver nanostructures,
thereby generating dense electromagnetic hotspots essential for ultrasensitive detection. Both
in-situ chemical reduction and ex-situ plasma-assisted infiltration strategies were employed to
precisely engineer nanoparticle distribution and interfacial coupling, achieving femtomolar
level detection limits (down to 0.1 fM) for model analytes. The resulting platforms exhibit
excellent spectral resolution, high analytical enhancement factors (~107), and outstanding
batch-to-batch reproducibility (RSD ~4.2%), fulfilling critical requirements for next
generation sensing technologies.
Importantly, TiO2-assisted photocatalytic regeneration enables efficient analyte removal and
signal recovery exceeding 93% over multiple reuse cycles, highlighting the platform’s
sustainability and long-term operational stability. Interfacial charge-transfer mechanisms
revealed through XPS analysis further underline the intelligent material design contributing to
sensor regeneration. Successful detection in complex real samples demonstrates the platform’s
applicability beyond laboratory conditions.
Overall, this work presents a future-ready SERS biosensing architecture that integrates
scalability, reusability, and ultrasensitivity within a single material system. The approach offers
a viable pathway toward next-generation, field-deployable biosensors for environmental
monitoring, biomedical diagnostics, and point-of-care analytical technologies.

Abstract: Advances in Point-of-Care Technologies for Biomedical Applications

 The incorporation of functionalized and modified micro- and nanostructures into biomedical applications has sparked significant research interest in recent years. Micro- and nanotechnology's potential in medicine and biomedical engineering is vast, encompassing areas such as implant and tissue engineering, as well as diagnosis and therapy. The current landscape demands the design of micro- and nanodevices that can effectively address biological challenges, leading to more efficient biomedical solutions. This presentation will focus on recent advancements in point-of-care (POC) systems developed within various European projects for handling and quantitative analysis. Specifically, the Hearten and KardiaTool projects aim to diagnose and monitor heart failure in patients by analyzing breath and saliva samples, enhancing analysis speed and efficiency while reducing sample and reagent consumption. These devices are capable of performing tasks such as sample pretreatment, separation, dilution, mixing, chemical reactions, detection, and product extraction. The entire analysis process can be fully automated, minimizing human involvement, preventing contamination, and ensuring repeatable experiments. Additionally, this presentation will address the challenges of scaling up POC devices, covering: (i) the fabrication of sensors and microfluidic devices, (ii) sensor functionalization to improve performance and capabilities, (iii) integration of existing detection techniques into the POC platform, and (iv) the development of quantitative readout extraction via handheld devices.

Background and Motivation:

Monitoring the functional health of older adults living independently in the community presents a significant biosensing challenge. Conventional clinical assessment is episodic and retrospective, relying on scheduled visits to evaluate wellbeing. This limits the ability to detect subtle but clinically meaningful changes in mobility, sleep, and daily routine, the earliest behavioural biomarkers of functional decline. This gap is particularly acute in regional and rural settings, where workforce constraints and geographic dispersion further reduce care visibility. Compounding this challenge, older adults frequently under-report symptoms due to fear of losing independence, further reducing opportunities for early clinical detection. There is a compelling need for continuous, passive biosensing solutions that capture real-world health signals without increasing burden on users, caregivers, or overstretched care systems.

Biosensing System Architecture:

Homeara+ is a smart ambient biosensing platform deployed in the homes of community-dwelling older adults in regional Australia. The system integrates a wireless network of passive environmental sensors, including passive infrared (PIR) motion detectors, door contact sensors, and environmental monitors to continuously capture behavioural biomarkers of daily living. Unlike wearable biosensors, Homeara+ requires no active user engagement: the sensor array operates unobtrusively within the existing home environment, generating high-frequency longitudinal data streams that reflect real-world patterns of mobility, activity, and sleep. This design makes the system particularly suited to populations who may not tolerate or sustain engagement with body- worn sensing devices.

Machine Learning and Behavioural Signal Analytics:

Raw sensor data are processed through a machine learning pipeline designed to extract and analyse individual behavioural patterns over time. The analytical framework identifies personalised baseline behavioural signatures and detects statistically meaningful deviations that may indicate changes in functional health status. Rather than generating discrete real-time alerts, the system applies retrospective pattern recognition to characterise gradual shifts, such as changes in sleeponset timing, reduced kitchen activity, or altered mobility trajectories across the home. This approach aligns with emerging paradigms in AI-enabled, data-driven biosensing, where clinical value derives not from isolated point-of-care measurements but from longitudinal behavioural signal analytics applied at the individual level.

Real-World Deployment and Implementation Findings:

Supported by ARIIA funding, this study deployed Homeara+ across multiple regional Australian households in partnership with community aged care providers and academic collaborators from the University of the Sunshine Coast and the University of California Davis Betty Irene Moore School of Nursing. Findings from real-world deployment highlight multi-level contextual factors, spanning policy, funding, service delivery, workforce capacity, and individual digital literacy that shape the translation of ambient biosensing technology into routine care practice. In response, the project developed a suite of open-access educational resources to support informed engagement with the technology across older adults, family caregivers, clinicians, and service providers.

Conclusion:

Homeara+ demonstrates that smart ambient biosensing, combined with machine learning-driven behavioural analytics, offers a viable and scalable pathway to continuous, non-intrusive functional health monitoring for older adults. This work advances the application of next-generation biosensing technologies in healthcare and personalised medicine, with implications for AI-enabled care models in community settings globally. It further contributes a real-world implementation perspective to the growing body of evidence on how ambient biosensing systems can be meaningfully embedded within complex health and social care environments.

Abstract: A New Paradigm for Assessing Strain Reliability in Flexible Organic Electronics

Flexible and wearable electronics demand transistor technologies that can sustain stable performance under extreme mechanical deformation. In this work, propose a quantitative benchmarking framework for strain resilience in organic thin-film transistors (OTFTs), introducing three normalized metrics: the Degradation Factor (DF), quantifying drain-current loss under strain; the Mobility Factor (MF), representing the rate of charge-transport degradation per unit strain; and the Strain-Stability Window (SSW), defining the maximum strain range within which devices remain in the safe operating zone (DF < 15%) . Using Silvaco Victory TCAD, systematically investigate the strain-dependent behaviour of single-dielectric (Al₂O₃) and hybrid-dielectric (Al₂O₃/PVP) OTFTs under both compressive (concave) and tensile (convex) bending with radii from 8 µm to 1 µm. Results show that hybrid dielectric OTFTs exhibit superior strain tolerance, with a degradation factor of only 9% under 9.85% tensile strain, compared to 25% for single-dielectric devices. Furthermore, hybrid devices show a markedly lower mobility factor ( -3 %/strain compressive, -1.9 %/strain tensile) compared with single-dielectric OTFTs (-6 %/strain compressive, -5 %/strain tensile). Beyond confirming the mechanical advantages of hybrid dielectrics, This work study demonstrates that strain-stability quantifiers provide a universal method to benchmark flexible OTFT reliability, bridging device physics with practical requirements of wearable bioelectronics. These findings establish hybrid Al₂O₃/PVP dielectrics not only as performance enhancers but also as reliable design enablers for next-generation strain-resilient organic electronics.