On-chip spectral imaging and sensing transition towards marketable technologies
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A computational spectrometer for the visible, near, and mid-infrared enabled by a single-spinning film encoder
Computational spectrometers enable low-cost, in-situ, and rapid spectral analysis, with applications in chemistry, biology, and environmental science. Traditional filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. Here we propose a computational spectrometer spanning visible to mid-infrared by combining the Single-Spinning Film Encoder (SSFE) with a deep learning-based reconstruction algorithm. Optimization through particle swarm optimization (PSO) allows for low-correlation and high-complexity spectral responses under different polarizations and spinning angles. The spectrometer demonstrates single-peak resolutions of 0.5 nm, 2 nm, 10 nm, and dual-peak resolutions of 3 nm, 6 nm, 20 nm for the visible, near, and mid-infrared wavelength ranges. Experimentally, it shows an average MSE of 1.05 × 10⁻³ for narrowband spectral reconstruction in the visible wavelength range, with average center-wavelength and linewidth errors of 0.61 nm and 0.56 nm. Additionally, it achieves an overall 81.38% precision for the classification of 220 chemical compounds, showcasing its potential for compact, cost-effective spectroscopic solutions.
On-chip solar power source for self-powered smart microsensors in bulk CMOS process
Enhancing the photoelectric conversion efficiency of on-chip solar cells is crucial for advancing solar energy harvesting in self-powered smart microsensors for Internet of Things applications. Here we show that adopting a center electrode (CE) layout instead of a ring electrode (RE) effectively reduces the shadowing effect of surface electrodes. Using a standard 0.18 μm CMOS process, we fabricated a 0.01 mm² segmented triple-well on-chip solar cell with CEs and highly doped interconnections. Measurements demonstrate a photoelectric conversion efficiency of 25.79% under solar simulator illumination, a 17.49% improvement over conventional designs. This on-chip solar cell is used for on-chip energy harvesting, achieving a maximum end-to-end conversion efficiency of 10.20%, referring to the overall efficiency from incident light power to load power output. The proposed energy harvesting system reliably provides a stable 1 V output to the load, even under varying illumination and load conditions.
Acoustic impedance-based surface acoustic wave chip for gas leak detection and respiratory monitoring
Acoustic impedance enables many interesting acoustic applications. However, acoustic impedance for gas sensing is rare and difficult. Here we introduce a micro-nano surface acoustic wave (SAW) chip based on the acoustic impedance effect to achieve ultra-fast and wide-range gas sensing. We theoretically established the relationship between surface load acoustic impedance and SAW attenuation, and analyzed the influence of acoustic impedance on acoustic propagation loss under different gas/humidity media. Experimental measurements reveal that the differences in acoustic impedance generated by different gases trigger different acoustic attenuation, and can achieve wide-range (0–100 v/v%) gas monitoring, with ultra-fast response and recovery speeds reaching sub-second levels (t90 < 1 s, t10 < 0.5 s) and detection limit of ~1 v/v%. This capability can also be perfectly utilized for human respiratory monitoring, accurately reflecting respiratory status, frequency, and intensity. Consequently, the SAW chip based on the acoustic impedance effect provides a new solution for in-situ detection of gas leaks and precise monitoring of human respiration.
Machine learning empowered coherent Raman imaging and analysis for biomedical applications
In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.
A gut-on-a-chip incorporating human faecal samples and peristalsis predicts responses to immune checkpoint inhibitors for melanoma
Patient responses to immune checkpoint inhibitors can be influenced by the gastrointestinal microbiome. Mouse models can be used to study microbiome–host crosstalk, yet their utility is constrained by substantial anatomical, functional, immunological and microbial differences between mice and humans. Here we show that a gut-on-a-chip system mimicking the architecture and functionality of the human intestine by including faecal microbiome and peristaltic-like movements recapitulates microbiome–host interactions and predicts responses to immune checkpoint inhibitors in patients with melanoma. The system is composed of a vascular channel seeded with human microvascular endothelial cells and an intestinal channel with intestinal organoids derived from human induced pluripotent stem cells, with the two channels separated by a collagen matrix. By incorporating faecal samples from patients with melanoma into the intestinal channel and by performing multiomic analyses, we uncovered epithelium-specific biomarkers and microbial factors that correlate with clinical outcomes in patients with melanoma and that the microbiome of non-responders has a reduced ability to buffer cellular stress and self-renew. The gut-on-a-chip model may help identify prognostic biomarkers and therapeutic targets.
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