The ion-sensitive field-effect transistor is a well-established electronic device typically used for pH sensing. The usability of the device for detecting other biomarkers in easily accessible biologic fluids, with dynamic range and resolution compliant with high-impact medical applications, is still an open research topic. Here, we report on an ion-sensitive field-effect transistor that is able to detect the presence of chloride ions in sweat with a limit-of-detection of 0.004 mol/m³. The device is intended for supporting the diagnosis of cystic fibrosis, and it has been designed considering two adjacent domains, namely the semiconductor and the electrolyte containing the ions of interest, by using the finite element method, which models the experimental reality with great accuracy. According to the literature explaining the chemical reactions that take place between the gate oxide and the electrolytic solution, we have concluded that anions directly interact with the hydroxyl surface groups and replace protons previously adsorbed from the surface. The achieved results confirm that such a device can be used to replace the traditional sweat test in the diagnosis and management of cystic fibrosis. In fact, the reported technology is easy-to-use, cost-effective, and non-invasive, leading to earlier and more accurate diagnoses.

Resonant label-free biosensors using silicon-on-insulator substrates represent a cutting-edge domain in optical sensing. These devices leverage the high refractive index contrast of silicon-on-insulator materials to create highly sensitive, compact sensors. They are crucial for real-time, precise biomolecular detection in healthcare diagnostics, environmental monitoring, and drug discovery, marking a significant stride in microphotonic technology and its practical applications. Different ring-based resonant configurations have been explored in this context, but a definitive comparative assessment of them was previously lacking in the literature. In our work, we experimentally compare four different ring configurations in terms of figure-of-merit, showing that the slotted ring performs better than competitive approaches. A silicon photonic chip including tens of ring resonators was fabricated and optically characterized considering air and aqueous solutions as cladding. The best sensitivity achieved was 64nm/RIU for the slotted ring configuration. The ring displayed a Q-factor in the order of 2.57 × 10 ⁴ , and the best estimated resolution was in the order of 10 ^–4 RIU in water. The measured figure-of-merit was grater than 200RIU ^–1 .

The persistent burden imposed by SARS-CoV-2, respiratory syncytial virus (RSV), and other respiratory viruses highlights the demand for real-time, label-free diagnostics capable of multi-pathogen detection. Despite numerous proposals, existing biosensors have yet to achieve the trifecta of sub-nanomolar sensitivity, simultaneous targeting, and a truly compact footprint. Here, we show a multiplexed photonic biosensor based on cascaded microring resonators that can detect two different viral glycoproteins, such as the SARS-CoV-2 spike protein and the RSV-F protein, at sub-nanomolar concentrations. Three strip-waveguide rings (radii = 99, 100, 101 μm; TE mode at 1310 nm) were designed via finite-element modelling and fabricated on a Si₃N₄-on-insulator platform using e-beam lithography and inductively coupled plasma reactive ion etching (ICP-RIE). Preliminary results demonstrate a quality factor of the order of 10⁵ and a mean bulk sensitivity value of 150 nm/RIU, evaluated in gradually concentrated ethanol solutions flowing on the sensor surface. A common bus waveguide enables wavelength-division multiplexing (WDM). Selective, oriented immobilization of anti-spike and anti-F antibodies is achieved through a protein A interfacial layer, while the central ring is intentionally left bare as an on-chip reference. By sweeping the wavelength of a continuous wave (CW) laser across each resonance, real-time resonance shifts are monitored and quantified via Lorentzian peak fitting. By functionalizing distinct rings with viral proteins-specific antibodies, our chip achieves detection limits of approximately 0.16 nM for the SARS-CoV-2 spike protein and 0.14 nM for the RSV-F protein, matching or surpassing the performance of conventional single-target optical biosensors. Our design allows the analysis of multiple viral antigens in parallel, reducing sample volume and overall test duration. Moreover, an on-chip reference ring ensures robust signal stability by compensating for non-specific effects, thereby enhancing accuracy and reproducibility. These findings underscore the promise of integrated photonics to reshape point-of-care diagnostics, especially in decentralized or resource-limited settings. We anticipate that this platform’s versatility can be extended to detect additional pathogens or biomolecules, providing a scalable and cost-effective tool for rapid clinical decision-making and epidemiological surveillance.

Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification. However, traditional approaches are not always applicable in a real-time environment as they do not integrate concrete traffic management after a malicious packet pattern has been identified. In this paper, a novel combined approach to both identify and discard potential malicious traffic in a real-time fashion is proposed. In detail, a Long Short-Term Memory (LSTM) supervised artificial neural network model is provided in which consecutive packet groups are considered as they flow through the corporate network. Moreover, the whole IDS architecture is modeled by a Petri Net (PN) that either blocks or allows packet flow throughout the network based on the LSTM model output. The novel hybrid approach combining LSTM with Petri Nets achieves a 99.71% detection accuracy—a notable improvement over traditional LSTM-only methods, which averaged around 97%. The LSTM–Petri Net approach is an innovative solution combining machine learning with formal network modeling for enhanced threat detection, offering improved accuracy and real-time adaptability to meet the rapid security needs of virtual environments and CPS. Moreover, the approach emphasizes the innovative role of the Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) as a form of “virtual sensing technology” applied to advanced network security. An extensive case study with promising results is provided by training the model with the popular IDS 2018 dataset.