Microalbuminuria can be detected using urine protein test strips. However, this approach can only be used for qualitative analysis. Moreover, despite the high accuracy of commercial instruments for the clinical detection albumin, these devices is not suitable to be used for home detection due to their bulky size and additional labeling steps. Therefore, our study sought to develop a label-free method by three types of miniaturized detector devices (MDDs) for the detection of HSA and BSA. Using flip-chip (FC) ultraviolet-C (UV–C) light-emitting diodes (FC UVC LEDs) as a light source. The most accurate of the five tested MDDs consisted of a FC UVC LED light source, a holder, and a quartz lenses (QL₁) with a light divergence angle of 16°. When detecting human serum albumin (HSA) and bovine serum albumin (BSA) at concentrations ranging from 0.01 to 4mg/mL, the coefficients of determination (R²) of absorbance and concentration at 279nm were 0.9850 and 0.9870, respectively. The FC UVC LED MDD designed herein was not only cost-effective but also had a small footprint and is therefore highly portable. The proposed approach for the quantitative detection of HSA and BSA is useful for early kidney disease screening.

Ptychography as a means of lensless imaging is used in wafer metrology applications using Extreme Ultraviolet (EUV) light, where use of high quality optics is out-of-scope. To obtain sufficient diffraction intensity, reflection geometries with shallow (ca. 20 degrees) grazing incidence angles are used, which require re-sampling the diffraction data in a process called tilted plane correction (TPC). The tilt angle used for TPC is conventionally obtained through either experimentally tricky calibration, manual estimation based on diffraction pattern symmetry, although computational approaches are emerging. In this work we offer an improved numerical optimization approach as an alternative to TPC, where we use the flexibility offered by our Automatic Differentiation (AD)-based ptychography approach to include the data resampling into the forward model to learn the tilt angle. We demonstrate convergence of the approach across a range of incidence angles on simulated and experimental data obtained on an EUV beamline with either a high-harmonic generation (HHG)-based or a visible light source.

We propose a method for separating fluorescence and Raman spectra in UV region using classical least squares. The method was applied to highly fluorescent samples. Since the spectral decomposition is based on many spectral data with different intensities, the fluorescence and Raman spectra were successfully resolved. As a result, the method effectively reduced the fluorescence interference in Raman spectral measurements.

Therapeutic drug monitoring (TDM) at the point of care is of paramount significance in healthcare. It enables timely monitoring of drug concentrations in a patient's body-fluids, especially in the bloodstream, ensuring personalized dosing for optimal therapeutic outcomes while minimizing side effects and toxicity. TDM is crucial for treating sepsis patients, as their altered pharmacokinetics make accurate antibiotic dosing challenging. Additionally, they need quick dose adjustments to match evolving clinical needs. TDM ensures individually optimal antibiotic levels for best efficacy. Recently, we exploited the advantages of deep-UV Raman spectroscopy in drug sensing of antibiotics for TDM. Firstly, deep-UV excitation enhances the Raman scattering, which is particularly useful for compounds with weak Raman signals, including low concentrated drugs. Secondly, it effectively minimizes fluorescence interference, a common challenge in conventional Raman spectroscopy. This makes deep-UV excitation especially suitable for monitoring active ingredients, such as fluoroquinolones and β-lactam antibiotics, in body fluids like urine and plasma at clinically relevant concentrations. Therefore, deep-UV Raman spectroscopy holds significant potential as a tool for personalized antibiotic dosing through rapid TDM at the point of care.

This study presents a biomolecule sensor utilizing ultraviolet plasmonic-enhanced native fluorescence, enhancing sensitivity and selectivity for detecting neurotransmitters (NTs). NTs, like monoamines, fluoresce weakly in the UV range. Plasmonic nanostructures, including Aluminum hole arrays, Aluminum nanocubes, and Al nanotriangles amplify UV fluorescence, and this biosensor improves NT detection, which is critical for understanding neurological disorders. Traditional methods lack multi-NT probing and molecule differentiation. Tested neurotransmitters include Tryptophan, Dopamine, Norepinephrine, and DOPAC. Multi-layered and monolayer silica microspheres increase sensitivity by 28 and 14 times compared to Si wafers. Furthermore, AL hole arrays and AL nanocubes could enhance the FL signal of three neurotransmitters (DA, NE, DOPAC) by 6 to 9 times. This paper highlights UV plasmonic-enhanced fluorescence's potential for distinguishing similar NT structures.

Next-generation metrology solutions in various technology areas require to image sample areas at the nanoscale. Coherent diffractive imaging based on ptychography is the route towards EUV imaging of nanostructures without lenses. A key component in a table-top EUV beamline is a high-brightness high-harmonic generation (HHG) source. Since our research is mainly directed towards wafer metrology for lithography in the semiconductor industry, we adhere to a reflection setup: the EUV light is scattered by the nanostructures at the surface of the sample, and is reflected towards a CCD camera, where a far-field diffraction pattern is recorded. A data-set comprising a multitude of these diffraction patterns is generated for partially overlapping positions of the focused probe on the sample. This provides the necessary redundancy for phase retrieval of the complex-valued field of the sample. Recent advancements in both hardware and software for computation enable the development of advanced algorithms. In particular, the benefits of automatic differentiation are exploited in order to cope with a drastic growth in model complexity. Our computational imaging algorithms realize wavelengthmultiplexed reconstruction and a modal approach for the spatial coherence of the source.

We report on advances in the fabrication of Josephson junctions, crucial devices in superconducting quantum circuits. In our previous work, we successfully fabricated these on 12-inch substrates using ArF immersion lithography. To enable future large-scale production, we are moving towards sputtering and dry etching techniques. After initial successful tests on a 4-inch substrate, we have now verified this process on 12-inch substrate fabrication equipment, marking significant progress despite the challenges we have faced.