Triboelectric nanogenerators have attracted wide attention due to their promising capabilities of scavenging the ambient environmental mechanical energy. However, efficient energy management of the generated high-voltage for practical low-voltage applications is still under investigation. Autonomous switches are key elements for improving the harvested energy per mechanical cycle, but they are complicated to implement at such voltages higher than several hundreds of volts. This paper proposes a self-sustained and automatic hysteresis plasma switch made from silicon micromachining, and implemented in a two-stage efficient conditioning circuit for powering low-voltage devices using triboelectric nanogenerators. The hysteresis of this microelectromechanical switch is controllable by topological design and the actuation of the switch combines the principles of micro-discharge and electrostatic pulling, without the need of any power-consuming control electronic circuits. The experimental results indicate that the energy harvesting efficiency is improved by two orders of magnitude compared to the conventional full-wave rectifying circuit.

Microplastics contaminating drinking water is a growing issue that has been the focus of a few recent studies, where a major bottleneck is the time-consuming analysis. In this work, a micro-optofluidic platform is proposed for fast quantification of microplastic particles, the identification of their chemical nature and size, especially in the 1–100 µm size range. Micro-reservoirs ahead of micro-filters are designed to accumulate all trapped solid particles in an ultra-compact area, which enables fast imaging and optical spectroscopy to determine the plastic nature and type. Furthermore, passive size sorting is implemented for splitting the particles according to their size range in different reservoirs. Besides, flow cytometry is used as a reference method for retrieving the size distribution of samples, where chemical nature information is lost. The proof of concept of the micro-optofluidic platform is validated using model samples where standard plastic particles of different size and chemical nature are mixed.

Microplastics are particulate water contaminants that are raising concerns regarding their environmental and health impacts. Optical spectroscopy is the gold standard for their detection; however, it has severe limitations such as tens of hours of analysis time and spatial resolution of more than 10 μm, when targeting the production of a 2D map of the microparticle population. In this work, through a single spectrum acquisition, we aim at quickly getting information about the whole population of identical particles, their chemical nature, and their size in a range below 20 μm. To this end, we built a compact setup enabling both attenuated total reflection Fourier transform infrared (ATR-FTIR) and Raman spectroscopy measurement on the same sample for comparison purposes. We used monodisperse polystyrene and poly(methyl methacrylate) microplastic spheres of sizes ranging between 6 and 20 μm, also measured collectively using a bench-top FTIR spectrometer in ATR mode. The ATR-FTIR technique appears to be more sensitive for the smallest particles of 6 μm, while the opposite trend is observed using Raman spectroscopy. We use theoretical modeling to simulate and explain the ripples observed in the measured spectra at the shortest wavelength (higher wavenumber) region, which appears as an indicator of the microparticle dimension. The latter finding opens new perspectives for ATR-FTIR for the identification and classification of populations of nearly identical micro-scale bodies, such as bacteria and other micro-organisms, where the same measured spectrum embeds dual information about the chemical nature and the size.

Optical spectrometers enable contactless chemical analysis. However, decreasing both their size and cost appears to be a prerequisite to their widespread deployment. Chip-scale implementation of optical spectrometers still requires tackling two main challenges. First, operation over a broad spectral range extending to the infrared is required to enable covering the molecular absorption spectrum of a broad variety of materials. This is addressed in our work with an Micro-Electro Mechanical Systems (MEMS)-based Fourier transform infrared spectrometer with an embedded movable micro-mirror on a silicon chip. Second, fine spectral resolution Δλ is also required to facilitate screening over several chemicals. A fundamental limit states that Δλ is inversely proportional to the mirror motion range, which cannot exceed the chip size. To boost the spectral resolution beyond this limit, we propose the concept of parallel (or multi-core) FTIR, where multiple interferometers provide complementary optical paths using the same actuator and within the same chip. The concept scalability is validated with 4 interferometers, leading to approximately 3 times better spectral resolution. After the atmospheric contents of a greenhouse gas are monitored, the methane absorption bands are successfully measured and discriminated using the presented device.

Imaging flow cytometry has been widely adopted in numerous applications such as optical sensing, environmental monitoring, clinical diagnostics, and precision agriculture. The system, with the assistance of machine learning, shows unprecedented advantages in automated image analysis, thus enabling high‐throughput measurement, identification, and sorting of biological entities. Recently, with the burgeoning developments of machine learning algorithms, deep learning has taken over most of data analysis and promised tremendous performance in intelligent imaging flow cytometry. Herein, an overview of the basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied to assist intelligent imaging flow cytometry is provided. Perspectives of emerging machine learning algorithms in implementing future intelligent imaging flow cytometry are also discussed.