Research Publications
Permanent URI for this collection
Browse
Recent Submissions
Item A DC-Coupled Neural Recording Analog Front-End with Bi-Level Bulk Modulation-Based EDO Compensation in 40nm Bulk CMOS(2025) Diez-Clos, Arnau; Huang, Xiaohua; Monna, Bert; Muratore, Dante G.This paper presents a compact, low-power analog front-end (AFE) for high-density micro-electrocorticography (μECoG) recordings. The AFE integrates a DC-coupled, chopper-stabilized low-noise boxcar sampler, a passive switched-capacitor low-pass filter, and a 10-bit single-slope analog-to-digital converter (ADC). The boxcar sampler minimizes noise folding, enhances anti-aliasing and reduces chopping ripple. To prevent AFE saturation, a novel electrode DC offset (EDO) compensation loop is introduced. It features an embedded digital-to-analog converter (DAC) by modulating the bulk terminals of the input transistors, and a bi-level compensation scheme, thereby eliminating the requirements for high-resolution digital low-pass filters and explicit DACs typically used in traditional DC servo loops. Simulation results in 40nm bulk CMOS show an input-referred noise of 1.69 μVrms over a 1-500 Hz bandwidth, an EDO compensation range of 110 mVpp, with a power consumption of 2.28 μW and an estimated area of 0.0028 mm2 per channelItem Multi-electroanalytical method capable, duty-cycled, 0.36 mm2 electrochemical frontend, achieving 170dB current sensing range with extended compliance voltage adopting feedforward cancellation(2025-04-17) Sukumaran, Amrith; Caruso, Francesco; Cattenoz, Regis; Putter, Bas; Nagel, Jean-Luc; Ravanilla, Ravanilla; Stergiou, Ioannis; Bouilly, Guillaume; Nussbaum, Pascal; Emery, StephaneItem Single-walled carbon nanotube biosensor for real-time monitoring of nitric oxide in inflammatory responses(2025-10-10) Zubkovs, Vitalijs; Belcastro, Laura; Sajjadi, Sayyed Hashem; Rabbani, Yahya; Ristaniemi, Aapo; Mdingi, Vuyisa; Peez, Christian; Tognato, Riccardo; Serra, Tiziano; Cattaneo, Stefano; Grad, Sibylle; Boghossian, Ardemis A.; Basoli, ValentinaOsteoarthritis (OA) is a degenerative inflammatory joint disease affecting millions of people worldwide. The early detection of OA and the continuous monitoring of its progression are essential for managing the disease. In this study, we develop an optical system for monitoring OA-related inflammation by detecting nitric oxide (NO), a molecule that is overproduced in joints during OA. The NO sensor is based on fluorescent single-walled carbon nanotubes (SWCNTs) coated with single-stranded DNA (ssDNA). The sensor fluorescence was characterized in the presence of cells and biological tissue using a custom-built optical shortwave infrared (SWIR) reader with LED excitation centered at 657 nm and 726 nm and emissions collected above 1000 nm. The ssDNA-SWCNTs were embedded in gelatin methacryloyl (GelMA) hydrogels to monitor the release of NO in inflamed (1 ng/mL IL1β) bovine chondrocytes over 48 h. The sensors show a concentration-dependent mechanical stability, maintaining a stable Young's modulus for at least 30 days at 1:10 ssDNA-SWCNT:GelMA mixing ratios (17.8 mg/L SWCNTs). The sensor was incorporated into a custom microfabricated sensor tag that was surgically inserted ex vivo into bovine and human knees. The reader measurements confirm measurable SWIR signal depths of up to 6 mm under the skin and 6 mm under muscle tissue. The measurements further confirm no significant sensor tag displacement after 2500 flexion knee cycles. The custom ssDNA-SWCNT sensor tag and reader thus demonstrate a potential pathway for integrating SWIR technologies into clinical and orthopedic applications.Item Taking AI-Based Side-Channel Attacks to a New Dimension(2025-10-15) Meier, Lucas David; Valencia, Felipe; Botocan, Cristian-Alexandru; Vizár, DamianThis paper revisits the Hamming Weight (HW) labelling function for machine learning assisted side channel attacks. Contrary to what has been suggested by previous works, our investigation shows that, when paired with modern deep learning architectures, appropriate pre-processing and normalization techniques; it can perform as well as the popular identity labelling functions and sometimes even beat it. In fact, we hereby introduce a new machine learning method, dubbed dimension 0, that helps solve the class imbalance problem associated to HW, while significantly improving the performance of unprofiled attacks. We additionally release our new, easy to use python package that we used in our experiments, implementing a broad variety of machine learning driven side channel attacks as open source, along with a new dataset AES_nRF, acquired on the nRF52840 SoC. The extended version of this publication is available under the following link: https://eprint.iacr.org/2025/655.pdf.Item High-throughput analysis of aqueous drug solubility, supersaturation, and aggregation behaviour using second harmonic light scattering(2025-09-22) Kalasová, Jitka; Tarun, Orly; Shynkarenko, Yevhen; Kuentz, MartinAqueous solubility is a crucial physicochemical property influencing drug absorption and bioavailability. Current solubility assays, whether assessing thermodynamic or kinetic solubility, involve trade-offs between accuracy, detection limit, speed, and resource consumption. Therefore, this study introduces a novel approach to drug solubility assessment based on non-resonant second harmonic scattering (SHS), which detects interfacial fluctuations of water molecules surrounding solutes. The apparent solubility of 14 poorly water-soluble model drugs was measured and compared to high pressure liquid chromatography (HPLC) data. Furthermore, the supersaturation propensity, defined as the ratio of solubility measured at one hour to that at 24 h, was evaluated for all 14 compounds. Lastly, the self-assembly behaviour was investigated, using sodium lauryl sulphate (SLS) as a reference system to benchmark micellization in the given forward-scattering SHS platform. The results showed a strong correlation between the SHS and HPLC solubility data (r = 0.9273). Supersaturation propensity was assessed and linked to the glass-forming ability and thermal properties of the drugs, whereby ketoconazole and tamoxifen exhibited the best supersaturation performance. Moreover, the critical micelle concentration of SLS appeared as a local minimum following a peak in SHS intensity, reflecting an increase in structural bulk centrosymmetry due to micelle formation. Similar micelle-like patterns were observed for five model drugs (i.e., amiodarone, felodipine, meclizine, tamoxifen, torcetrapib), suggesting the formation of self-assembled structures at concentrations above the solubility limit. These findings demonstrate the potential of non-resonant SHS as a promising analytical tool for solubility determination, offering a versatile, dynamic and high-throughput format with minimal compound and solvent consumption, while also providing insights into drug aggregation or self-assembly at the molecular level.