We report a compact mid-infrared (MIR) photothermal spectroscopic ethane (C2H6) sensor with a hollow-core negative-curvature-fiber (HC-NCF) gas cell. The HC-NCF supports low-loss transmission of an MIR pump (3.348 µm) and a near-infrared (NIR) probe (1.55 µm). The pump and probe laser beams are launched into the gas cell from the opposite ends of the HC-NCF, allowing independent MIR pump delivery and NIR fiber-optic probe circuitry. The use of Fabry-Perot as the probe interferometer simplifies the sensor design and suppresses the common-mode noise in the lead in/out single-mode fiber. With a 14-cm-long HC-NCF, an ethane sensor system with the limit of detection (LOD) of 13 parts-per-billion (ppb) is achieved with 1 s lock-in time constant. The LOD goes down to 2.6 ppb with 410 s average time, which corresponds to noise equivalent absorption (NEA) of 2.0×10−6 and is a record for the hollow-core fiber MIR gas sensors. The system instability is 2.2% over a period of 8 hours.

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Dual-comb spectroscopy (DCS) has revolutionized optical spectroscopy by providing broadband spectral measurements with unprecedented resolution and fast response. Photothermal spectroscopy (PTS) with a pump-probe configuration offers a highly sensitive gas sensing method, which is normally performed using a single-wavelength pump laser. The merging of PTS with DCS may enable a spectroscopic method by taking advantage of both technologies, which has never been studied yet. Here, we report dual-comb photothermal spectroscopy (DC-PTS) by passing dual combs and a probe laser through a gas-filled anti-resonant hollow-core fiber, where the generated multi-heterodyne modulation of the refractive index is sensitively detected by an in-line interferometer. As an example, we have measured photothermal spectra of acetylene over 1 THz, showing a good agreement with the spectral database. Our proposed DC-PTS provides opportunities for broadband gas sensing with super-fine resolution and high sensitivity, as well as with a small sample volume and compact configuration.

<div><div><div><p>The conventional mental healthcare regime follows a reactive, symptom-focused, and episodic approach in a non-continuous manner, wherein the individual discretely records their biomarker levels or vital signs for a short period prior to a subsequent doctor’s visit. Recognizing that each individual is unique and requires continuous stress monitoring and personally tailored treatment, we propose a holistic hybrid edge-cloud Wearable Internet of Things (WIoT)-based online stress monitoring solution to address the above needs. To eliminate the latency associated with cloud access, appropriate edge models—Spiking Neural Network (SNN), Conditionally Parameterized Convolutions (CondConv), and Support Vector Machine (SVM)—are trained, enabling low-energy real-time stress assessment near the subjects on the spot. This work leverages design-space exploration for the purpose of optimizing the performance and energy efficiency of machine learning inference at the edge. The cloud exploits a novel multimodal matching network model that outperforms six state-of-the-art stress recognition algorithms by 2-7% in terms of accuracy. An offloading decision process is formulated to strike the right balance between accuracy, latency, and energy. By addressing the interplay of edge-cloud, the proposed hierarchical solution leads to a reduction of 77.89% in response time and 78.56% in energy consumption with only a 7.6% drop in accuracy compared to the IoT-Cloud scheme, and it achieves a 5.8% increase in accuracy on average compared to the IoT-Edge scheme.</p></div></div></div>