To overcome the parasitic absorption of UV light in the transparent conductive oxide layer of flexible CIGS solar cells, a CsPbBr ₃ perovskite nanocrystal based luminescent down-shifting layer was integrated on flexible CIGS solar cells.

Although the high incidence and recurrence rates of urothelial cancer of the bladder (UCB) are heavy burdens, a noninvasive tool for effectively detecting UCB as an alternative to voided urine cytology, which has low sensitivity, is yet to be reported. Herein, we propose an intelligent discrimination method between normal (SV-HUC-1) and cancerous (TCCSUP) urothelial cells by using a combination of micro-dimensional electrochemical impedance spectroscopy (µEIS) with machine learning (ML) for a noninvasive and high-accuracy UCB diagnostic tool. We developed a unique valved flow cytometry, equipped with a pneumatic valve to increase sensitivity without cell clogging. Since contact between a cell and electrodes is tight with a high volume fraction, the electric field can be effectively confined to the cell. This enables the proposed sensor to highly discriminate different cell types at frequencies of 10, 50, 100, 500 kHz, and 1 MHz. A total of 236 impedance spectra were applied to six ML models, and systematic comparisons of the ML models were carried out. The hyperparameters were estimated by conducting a grid search or Bayesian optimization. Among the ML models, random forest strongly discriminated between SV-HUC-1 and TCCSUP, with an accuracy of 91.7%, sensitivity of 92.9%, precision of 92.9%, specificity of 90%, and F1-score of 93.8%.

Demand for flexible transparent sensors for futuristic cars is increasing since such sensors can enhance the freedom of design and aesthetic value in the interior of cars. Herein, we propose a unique roll-to-roll UV lamination process that can expedite large-scale Ag nanowire (AgNW) transfer for a flexible capacitive sensor, using a photocurable resin composed of an epoxy acrylate oligomer, a reactive monomer (1,6-hexanediol diacrylate), and a photoinitiator (1-hydroxycyclohexyl phenyl ketone). The acryl groups in the resin were rapidly crosslinked by UV irradiation, which facilitated the AgNWs transfer from a PET to a PC substrate with the speed of 1050 cm² min⁻¹ and enhanced the adhesion between the AgNWs and the PC substrate. Systematic experiments were performed to determine optimal fabrication parameters with respect to the UV dose, lamination pressure, and laser dicing conditions. At the optimal fabrication conditions, the sheet resistance of AgNWs on a PC film (PC-AgNW) was as small as 36.79 Ω sq⁻¹, which was only 3.17% deviation from that on a PET film (PET-AgNW). Furthermore, the optical transmittance of the PC-AgNW exceeded 88% over the visible range, and it was greater than that of the PET-AgNW. Notably, the sheet resistance of the PC-AgNW was almost constant after 50 taping and peeling cycles, indicating remarkable adhesion to the substrate. Furthermore, a capacitive touch sensor was fabricated using the PC-AgNW, and its switching signals were presented with and without finger touch.

Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.

We report an anti-reflective cover glass for Cu(In,Ga)Se₂ (CIGS) thin film solar cells. Subwavelength structures (SWSs) were fabricated on top of a cover glass using one-step self-masked etching. The etching method resulted in dense whiskers with high aspect ratio. The produced structure exhibited excellent anti-reflective properties over a broad wavelength range, from the ultraviolet to the near infrared. Compared to a flat-surface glass, the average transmittance of the glass integrated with the SWSs improved from 92.4% to 95.2%. When the cover glass integrated with the SWSs was mounted onto the top of a CIGS device, the short-circuit current and the efficiency of the solar cell were enhanced by 4.38 and 6%, respectively, compared with a CIGS solar cell without cover glass.