Speakers

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.

Abstract: From Biological Signals to Intelligent Decisions: AI and Machine Learning in Biosensors

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into biosensor
systems is redefining how biological signals are acquired, interpreted, and transformed into
actionable information. Biosensors play a crucial role in modern applications such as
healthcare diagnostics, environmental monitoring, food safety, and wearable technologies.
However, conventional biosensing approaches often face limitations related to signal noise,
variability of biological samples, limited sensitivity, and difficulties in real-time
interpretation. The rapid growth of AI and ML offers powerful solutions to these challenges,
enabling biosensors to evolve from passive detection devices into intelligent, adaptive, and
data-driven systems.
The motivation for this presentation arises from the increasing availability of complex
biosensor data and the pressing need for computational methods capable of extracting
meaningful patterns from heterogeneous and high-dimensional signals. Biological data are
inherently noisy and context-dependent, making traditional analytical techniques
insufficient for robust decision-making. AI-based approaches, particularly machine learning
and deep learning models, provide the capability to learn from data, identify subtle
correlations, and continuously improve performance as new data become available. This
paradigm shift is essential for advancing biosensors toward higher accuracy, autonomy, and
scalability.
This talk presents a structured AI-driven framework for biosensor data analysis and system
enhancement. The proposed approach begins with data preprocessing techniques designed
to handle noise, missing values, and signal variability commonly found in biosensor
measurements. Feature extraction and selection methods are then applied to identify the
most informative characteristics of biological signals. Supervised and unsupervised machine
learning models are employed to perform classification, regression, and anomaly detection
tasks, while deep learning architectures are explored for complex pattern recognition and
time-series analysis. Emphasis is placed on model validation, performance evaluation, and
computational efficiency to ensure real-world applicability.
Key insights from recent studies and applied scenarios demonstrate that AI-enhanced
biosensors achieve significant improvements in sensitivity, specificity, and robustness
compared to traditional systems. Examples include more accurate detection of biomarkers,
improved monitoring of physiological parameters in wearable devices, and enhanced
predictive capabilities in dynamic environments. Additionally, AI techniques contribute to
intelligent biosensor design by supporting optimization processes, adaptive calibration, and
predictive modeling of sensor behavior under varying conditions. Beyond technical
performance, the presentation addresses critical challenges associated with the adoption of
AI in biosensing. These include data quality and availability, model interpretability, ethical
considerations, and integration with existing clinical and industrial workflows. Strategies for
explainable AI, standardized data pipelines, and interdisciplinary collaboration are discussed
as essential components for responsible and sustainable deployment.
In conclusion, the convergence of AI, ML, and biosensor technologies represents a
transformative step toward intelligent sensing systems capable of supporting timely,
accurate, and autonomous decision-making. By bridging computer engineering, data science,
and biosensor technology, this work highlights how AI can act as a catalyst for innovation,
accelerating the development of next-generation biosensors with meaningful impact across
scientific, industrial, and societal domains.

Abstract: Carbon quantum dots (CQDs) in forensic investigations: a review of current
applications and future perspectives

The advent of Carbon Quantum Dots (CQDs) has introduced transformative possibilities
in forensic science, addressing longstanding challenges in the detection, analysis, and
preservation of trace evidence. This review comprehensively examines CQDs,
highlighting their synthesis methodologies, unique physicochemical properties, and
diverse applications in forensic investigations. Emphasizing green, scalable, and cost
effective synthesis routes, the review explores CQDs' tunable fluorescence, exceptional
optical characteristics, and biocompatibility, which contribute to their superior
performance in forensic contexts. Specifically, CQDs have shown significant promise in
areas such as crime scene analysis, fingerprint enhancement, drug identification, and
toxicology, offering enhanced sensitivity, specificity, and precision in evidence detection.
Despite their potential, the integration of CQDs into forensic workflows faces hurdles
related to reproducibility, standardization, and regulatory compliance. Moreover, the
convergence of CQDs with cutting-edge technologies like artificial intelligence and
computational simulations presents an exciting frontier for advancing forensic
methodologies, minimizing human error, and ensuring high throughput and accuracy in
investigative processes. This review not only underscores the potential of CQDs to
revolutionize forensic science but also identifies key challenges and proposes future
directions for research, focusing on refining CQD-based applications and fostering
seamless integration into forensic protocols. In summary, CQDs represent a promising
and versatile toolset for the future of forensic investigations, driving significant
improvements in analytical precision and efficiency.

Abstract:
Climate-smart agriculture (CSA) emphasizes sustainable practices that enhance productivity, resilience, and environmental stewardship in the face of climate change. A critical enabler of CSA is the integration of advanced sensing technologies, particularly biosensors, which provide real-time, precise, and cost-effective monitoring of agricultural systems. Biosensors—analytical devices that couple biological recognition elements with transducers—offer unique advantages in detecting soil nutrients, water quality, plant health, and greenhouse gas emissions. Their application in agriculture enables farmers to optimize resource use, reduce input waste, and mitigate environmental impacts. For instance, soil nutrient biosensors can guide site-specific fertilizer application, while plant stress biosensors detect early physiological changes caused by drought, salinity, or pathogen attack, allowing timely interventions. Additionally, biosensors for methane and nitrous oxide monitoring contribute to quantifying and reducing agricultural emissions, aligning with global climate goals. Recent advances in nanotechnology, microfluidics, and wireless communication have further enhanced biosensor sensitivity, portability, and integration into digital farming platforms. When combined with data analytics and IoT-based decision support systems, biosensors create a feedback loop that empowers farmers with actionable insights, fostering resilience against climate variability. Despite challenges such as scalability, cost, and durability under field conditions, biosensors represent a transformative tool for achieving sustainable intensification and climate-smart outcomes. In conclusion, the biosensors play a pivotal role in bridging biological processes with precision agriculture, underscoring their potential to revolutionize monitoring, management, and sustainability in modern farming systems.

Abstract: Aptamer functionalized photonic crystal sensor for rapid detection of sars-coronavirus-2

Murtaza is an innovative researcher specializing in point-of-care testing, such as bioinspired photonic
crystal materials, aptamer-functionalized systems, and stimuli-responsive platforms for diagnostics,
biotechnology, and disease profiling. He designs ssDNA aptamers targeting protein biomarkers derived
from human samples and from viruses, e.g., SARS-CoV-2 and HIV, while investigating their structural
modulation. As a leader of multiple funded projects, he develops advanced optical point-of-care sensors
for virus detection. His expertise extends to integrating smart-responsive polymers into sensing
technologies for environmental applications, such as detecting pathogens and heavy metal ions in
biological and water samples. His work is featured in prestigious journals of ELSEVIER, ACS, RSC, and Wiley.

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.

Abstract: Deep Learning-assisted Quantification of Optical Signals from Paper Immunoassays.

Paper-based immunoassays are widely used point-of-care diagnostic tools for the detection of specific biomarkers. Such paper-immunoassays typically provide only a qualitative YES/NO visual result, indicated by the presence or absence of colored test and control lines formed by optical signals such as colorimetric changes on a paper strip. These simple readouts are fast and user-friendly but often lack quantitative analysis. Detection using external reader, or attachments in the smartphones are proposed that can capture and analyse the optical signals. However, the major drawbacks of existing smartphone solutions are brand-specific or assay-specific, requiring specific apps or hardware for different test types. Thus, the quantification system that can accurately identify and quantify multiple brands and types of paper-based immunoassays without needing external hardware remains a challenge.

Addressing this limitation, this work also introduced a generic, smartphone-compatible algorithm driven by convolutional neural networks (CNNs) for quantitative interpretation of colorimetry signals from the paper devices across (i) different brands; (ii) different assays, and (iii) compatible with environmental conditions, as well as device orientations. Validation involved diverse commercial kits such as pregnancy kits and ovulation kits, imaged using different brands of smartphone. The framework developed provides an end-to-end solution that extracts image features, maps the intensity values to sample concentrations, generating calibration curves for accurate analyte quantification. Further, our framework was tested on in-house designed competitive immunoassays for vitamin D₃ detection and quantification. This work highlights advancements biosensors designs with digital innovation for AI-powered smartphone diagnostics, overcoming traditional limitations of rapid diagnostic tests.