Xingjie Liu | Searcharxiv

arXiv2026

MEMS Fiber-Tip Photoacoustic Spectrometer for In Situ Microscale Trace Gas Sensing

To meet the stringent requirements for miniaturized and highly sensitive trace gas sensing in space-constrained scenarios, including power equipment monitoring, minimally invasive biomedical diagnostics, and in situ lithium-battery analysis, we report a MEMS-integrated fiber-tip photoacoustic spectrometer (MFPAS). The device incorporates a Fabry-Perot (F-P) photoacoustic sensor formed by directly butt-coupling a single-mode fiber (SMF) to a 3 mm x 3 mm MEMS chip with a 100-nm-thick low-pressure chemical vapor deposition (LPCVD) Si$_3$N$_4$ diaphragm. The resulting approximately 200-$μ$m deep silicon microcavity functions simultaneously as a photoacoustic gas cell and an acoustic confinement cavity. A micro-aperture fabricated at the diaphragm periphery by focused ion beam (FIB) milling serves as both a gas diffusion channel and an acoustic high-pass filter, suppressing ambient low-frequency pressure fluctuations and stabilizing the F-P quadrature point without active servo control. In gas-phase measurements, the sensor achieves a noise-equivalent concentration (NEC) of 58.5 ppb@1s, with a rapid response time of 6 s. Benefiting from its ultra-small cavity volume of approximately 1.5 nL, the device is further adapted through structural packaging for in situ dissolved gas analysis in transformer oil, where it achieves an NEC of 230 ppb@1s and a T90 response time of 320 s in the oil phase. By combining nanoliter-scale detection volume, ppb-level sensitivity, rapid response, and wafer-scale batch fabrication compatibility, the proposed MFPAS bridges MEMS diaphragm micromachining and FIB-enabled gas exchange engineering. This design overcomes the intrinsic gas-exchange limitation of conventional sealed-diaphragm optical microphones and offers significant potential for power equipment monitoring and in situ health diagnostics.

physics.optics↗

arXiv2011

Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach

In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To address the issue of hidden social influence, we devise new algorithms to learn the model parameters of our proposal based on expectation maximization (EM). In addition to a single-machine version of our EM algorithm, we further devise a parallelized implementation on the Map-Reduce framework to process two large-scale datasets we collect. Moreover, we show that the social influence obtained from our generative models can be used for group recommendation. Finally, we conduct comprehensive experiments using the datasets crawled from last.fm and whrrl.com to validate our ideas. Experimental results show that the generative models with social influence significantly outperform those without incorporating social influence. The unified generative model proposed in this paper obtains the best performance. Moreover, our study on social influence finds that users in whrrl.com are more likely to get influenced by friends than those in last.fm. The experimental results also confirm that our social influence based group recommendation algorithm outperforms the state-of-the-art algorithms for group recommendation.

cs.SI↗