Ultrahigh Passive Cooling Power in Hydrogel with Rationally Designed Optofluidic Properties.

The cooling power of a radiative cooler is more than halved in the tropics, e.g., Singapore, because of its harsh weather conditions including high humidity (84% on average), strong downward atmospheric radiation (∼40% higher than elsewhere), abundant rainfall, and intense solar radiation (up to 1200 W/m2 with ∼58% higher UV irradiation). So far, there has been no report of daytime radiative cooling that well achieves effective subambient cooling. Herein, through integrated passive cooling strategies in a hydrogel with desirable optofluidic properties, we demonstrate stable subambient (4-8 °C) cooling even under the strongest solar radiation in Singapore. The integrated passive cooler achieves an ultrahigh cooling power of ∼350 W/m2, 6-10 times higher than a radiative cooler in a tropical climate. An in situ study of radiative cooling with various hydration levels and ambient humidity is conducted to understand the interaction between radiation and evaporative cooling. This work provides insights for the design of an integrated cooler for various climates.

A disposable optofluidic micro-transmission cell with tailorable length for Fourier-transform infrared spectroscopy of biological fluids.

Mid-infrared Fourier-transform infrared (FT-IR) spectroscopy of liquid biological samples is limited by the high absorption of water in this spectral range, which makes conventional transmission cuvettes unsuitable as their centimeter-scale length is already too big. The most common alternative relies on the use of attenuated total reflection (ATR) accessories, which provide a small interaction path length for light along the interface between the analyte and the expensive ATR crystals. In this work, we address this issue by proposing a disposable and low-cost micro-transmission cell. Its construction relies on a simple technique, which consists of dispersing plastic spherical microparticles in a liquid sample before dispensing it between two pieces of silicon assembled one onto the other and acting as windows for the cell. Consequently, the microparticles act as a spacer of very precise height in-between the two silicon windows. This technique allows easy construction of infrared absorption cells with near-optimum optical interaction path length just by selecting the most appropriate particle size. The concept is demonstrated by measuring the concentration of glucose in aqueous solutions using microspheres of diameter 20 µm then 40 µm and analyzing the corresponding glucose absorption peaks in the wavenumber range 950-1200 cm-1. The performance is compared to that of standard ATR spectroscopy of the same samples. This resulted in a root-mean-square error of cross-validation (RMSECV) of 58.8 mg dl-1 as obtained for transmission measurements by partial least squares (PLS) regression, which is comparable to the RMSECV of 53 mg dl-1 for single-reflection diamond ATR measurements.

Single Infrared Spectrum Enables Simultaneous Identification of (Bio)Chemical Nature and Particle Size of Microorganisms and Synthetic Microplastic Beads.

Populations of nearly identical chemical and biological microparticles include the synthetic microbeads used in cosmetic, biomedical, agri-food, and pharmaceutical industries as well as the class of living microorganisms such as yeast, pollen, and biological cells. Herein, we identify simultaneously the size and chemical nature of spherical microparticle populations with diameters larger than 1 µm. Our analysis relies on the extraction of both physical and chemical signatures from the same optical spectrum recorded using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. These signatures are the spectral resonances caused by the microparticles, which depend on their size and the absorption peaks revealing their chemical nature. We validate the method first on separated and mixed groups of spherical microplastic particles of two different diameters, where the method is used to calculate the diameter of the microspherical particles. Then, we apply the method to correctly identify and measure the diameter of Saccharomyces cerevisiae yeast cells. Theoretical simulations to help in understanding the effect of size distribution and dispersion support our results.

Potential of a Miniature Spectral Analyzer for District-Scale Monitoring of Multiple Gaseous Air Pollutants.

Gas sensors that can measure multiple pollutants simultaneously are highly desirable for on-site air pollution monitoring at various scales, both indoor and outdoor. Herein, we introduce a low-cost multi-parameter gas analyzer capable of monitoring multiple gaseous pollutants simultaneously, thus allowing for true analytical measurement. It is a spectral sensor consisting of a Fourier-transform infrared (FTIR) gas analyzer based on a mid-infrared (MIR) spectrometer. The sensor is as small as 7 × 5 × 2.5 cm3. It was deployed in an open-path configuration within a district-scale climatic chamber (Sense City, Marne-la-Vallée, France) with a volume of 20 × 20 × 8 m3. The setup included a transmitter and a receiver separated by 38 m to enable representative measurements of the entire district domain. We used a car inside the climatic chamber, turning the engine on and off to create time sequences of a pollution source. The results showed that carbon dioxide (CO2) and water vapor (H2O) were accurately monitored using the spectral sensor, with agreement with the reference analyzers used to record the pollution levels near the car exhaust. Furthermore, the lower detection limits of CO, NO2 and NO were assessed, demonstrating the capability of the sensor to detect these pollutants. Additionally, a preliminary evaluation of the potential of the spectral sensor to screen multiple volatile organic compounds (VOCs) was conducted at the laboratory scale. Overall, the results demonstrated the potential of the proposed multi-parameter spectral gas sensor in on-site gaseous pollution monitoring.

Substrate Signal Inhibition in Raman Analysis of Microplastic Particles.

In Raman analysis, the substrate material serves very often for signal enhancement, especially when metallic surfaces are involved; however, in other cases, the substrate has an opposite effect as it is the source of a parasitic signal preventing the observation of the sample material of interest. This is particularly true with the advent of microfluidic devices involving either silicon or polymer surfaces. On the other hand, in a vast majority of Raman experiments, the analysis is made on a horizontal support holding the sample of interest. In our paper, we report that a simple tilting of the supporting substrate, in this case, silicon, can drastically decrease and eventually inhibit the Raman signal of the substrate material, leading to an easier observation of the target analyte of the sample, in this case, microplastic particles. This effect is very pronounced especially when looking for tiny particles. Explanation of this trend is provided thanks to a supporting experiment and further numerical simulations that suggest that the lensing effect of the particles plays an important role. These findings may be useful for Raman analysis of other microscale particles having curved shapes, including biological cells.

Detection of Sub-20 µm Microplastic Particles by Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Comparison with Raman Spectroscopy.

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.

Spatiotemporal dynamics of nanowire growth in a microfluidic reactor.

Co-integration of nanomaterials into microdevices poses several technological challenges and presents numerous scientific opportunities that have been addressed in this paper by integrating zinc oxide nanowires (ZnO-NWs) into a microfluidic chamber. In addition to the applications of these combined materials, this work focuses on the study of the growth dynamics and uniformity of nanomaterials in a tiny microfluidic reactor environment. A unique experimental platform was built through the integration of a noninvasive optical characterization technique with the microfluidic reactor. This platform allowed the unprecedented demonstration of time-resolved and spatially resolved monitoring of the in situ growth of NWs, in which the chemicals were continuously fed into the microfluidic reactor. The platform was also used to assess the uniformity of NWs grown quickly in a 10-mm-wide microchamber, which was intentionally chosen to be 20 times wider than those used in previous attempts because it can accommodate applications requiring a large surface of interaction while still taking advantage of submillimeter height. Further observations included the effects of varying the flow rate on the NW diameter and length in addition to a synergetic effect of continuous renewal of the growth solution and the confined environment of the chemical reaction.

Tailoring silicon for dew water harvesting panels.

Dew water, mostly ignored until now, can provide clean freshwater resources, just by extracting the atmospheric vapor available in surrounding air. Inspired by silicon-based solar panels, the vapor can be harvested by a concept of water condensing panels. Efficient water harvesting requires not only a considerable yield but also a timely water removal from the surface since the very beginning of condensation to avoid the huge evaporation losses. This translates into strict surface properties, which are difficult to simultaneously realize. Herein, we study various functionalized silicon surfaces, including the so-called Black Silicon, which supports two droplet motion modes-out-of-plane jumping and in-plane sweeping, due to its unique surface morphology, synergistically leading to a pioneering combination of above two required characteristics. According to silicon material's scalability, the proposed silicon-based water panels would benefit from existing infrastructures toward dual functions of energy harvesting in daytime and water harvesting in nighttime.

A microfluidic chip enables fast analysis of water microplastics by optical spectroscopy.

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.

All-dielectric orthogonal doublet cylindrical metalens in long-wave infrared regions.

Metalens have been recently introduced to overcome shortcomings of traditional lenses and optical systems, such as large volume and complicated assembly. As a proof-of-principle demonstration, we design an all-dielectric converging cylindrical metalens (CML) for working in long-wave infrared regions around 9 µm, which is made up of silicon-pillar on MgF2 dielectric layer. We further demonstrate the focusing effect of an orthogonal doublet cylindrical metalens (ODCM). Two CMLs are combined orthogonally and a circular focusing spot was demonstrated. This proves that within a certain size range, the focusing effect achieved by the ODCM is similar to that of a traditional circular metalens.

Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection.

Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy >99.6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high-speed analysis reaches 346 frames per second, outperforming the state-of-the-art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.

Rare bioparticle detection via deep metric learning.

Recent deep neural networks have shown superb performance in analyzing bioimages for disease diagnosis and bioparticle classification. Conventional deep neural networks use simple classifiers such as SoftMax to obtain highly accurate results. However, they have limitations in many practical applications that require both low false alarm rate and high recovery rate, e.g., rare bioparticle detection, in which the representative image data is hard to collect, the training data is imbalanced, and the input images in inference time could be different from the training images. Deep metric learning offers a better generatability by using distance information to model the similarity of the images and learning function maps from image pixels to a latent space, playing a vital role in rare object detection. In this paper, we propose a robust model based on a deep metric neural network for rare bioparticle (Cryptosporidium or Giardia) detection in drinking water. Experimental results showed that the deep metric neural network achieved a high accuracy of 99.86% in classification, 98.89% in precision rate, 99.16% in recall rate and zero false alarm rate. The reported model empowers imaging flow cytometry with capabilities of biomedical diagnosis, environmental monitoring, and other biosensing applications.

Machine Learning-Based Pipeline for High Accuracy Bioparticle Sizing.

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.

Editorial for the Special Issue on IMCO 2019.

This special issue is a collection of 12 technical papers and two reviews that are expanded into full-length articles from the conference abstracts of the 9th International Multidisciplinary Conference on Optofluidics (IMCO 2019) held in Hong Kong in 14-17 June 2019 [...].

Employing a MEMS plasma switch for conditioning high-voltage kinetic energy harvesters.

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.

Silicon Multi-Pass Gas Cell for Chip-Scale Gas Analysis by Absorption Spectroscopy.

Semiconductor and micro-electromechanical system (MEMS) technologies have been already proved as strong solutions for producing miniaturized optical spectrometers, light sources and photodetectors. However, the implementation of optical absorption spectroscopy for in-situ gas analysis requires further integration of a gas cell using the same technologies towards full integration of a complete gas analysis system-on-chip. Here, we propose design guidelines and experimental validation of a gas cell fabricated using MEMS technology. The architecture is based on a circular multi-pass gas cell in a miniaturized form. Simulation results based on the proposed modeling scheme helps in determining the optimum dimensions of the gas cell, given the constraints of micro-fabrication. The carbon dioxide spectral signature is successfully measured using the proposed integrated multi-pass gas cell coupled with a MEMS-based spectrometer.

Continuous Monitoring of Air Purification: A Study on Volatile Organic Compounds in a Gas Cell.

Air pollution is one of the major environmental issues that humanity is facing. Considering Indoor Air Quality (IAQ), Volatile Organic Compounds (VOCs) are among the most harmful gases that need to be detected, but also need to be eliminated using air purification technologies. In this work, we tackle both problems simultaneously by introducing an experimental setup enabling continuous measurement of the VOCs by online absorption spectroscopy using a MEMS-based Fourier Transform infrared (FTIR) spectrometer, while those VOCs are continuously eliminated by continuous adsorption and photocatalysis, using zinc oxide nanowires (ZnO-NWs). The proposed setup enabled a preliminary study of the mechanisms involved in the purification process of acetone and toluene, taken as two different VOCs, also typical of those that can be found in tobacco smoke. Our experiments revealed very different behaviors for those two gases. An elimination ratio of 63% in 3 h was achieved for toluene, while it was only 14% for acetone under same conditions. Adsorption to the nanowires appears as the dominant mechanism for the acetone, while photocatalysis is dominant in case of the toluene.

Rapid assessment of nanomaterial homogeneity reveals crosswise structural gradients in zinc-oxide nanowire arrays.

In an effort to scale-up nanomaterial growth over large surface areas, we aim to effectively study the structural non-homogeneities within the arrays of zinc oxide nanowires (ZnO-NWs). The assessment of the lateral gradient of the nanowires' characteristics is presented including their height and surface density. To this end, spectroscopic ellipsometry and the rather recently reported technique of spectral domain attenuated reflectometry are used as two fast, simple and non-invasive characterization methods with further capabilities of scanning over the sample surface. Simple models are proposed by considering ZnO-NWs as the equivalent of thin stratified layers based on the effective medium approach. The methodology not only reveals the presence of gradients, but also enables quantitative analysis for all the samples grown using the hydrothermal method with different growth times ranging from 0.5 h up to 4 h. The gradients are confirmed using scanning electron microscopy (SEM) observations taken as a reference. The results also suggest that the sample orientation during the growth influences the NW growth besides the other parameters already known to affect the growth mechanisms.

On-chip parallel Fourier transform spectrometer for broadband selective infrared spectral sensing.

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.

Regulation of lipid droplets in live preadipocytes using optical diffraction tomography and Raman spectroscopy.

Lipid droplets have gained strong interest in recent years to comprehend how they function and coordinate with other parts of the cell. However, it remains challenging to study the regulation of lipid droplets in live preadipocytes using conventional microscopic techniques. In this paper, we study the effects of fatty acid stimulation and cell starvation on lipid droplets using optical diffraction tomography and Raman spectroscopy by measuring size, refractive index, volume, dry mass and degree of unsaturation. The increase of fatty acids causes an increase in the number and dry mass of lipid droplets. During starvation, the number of lipid droplets increases drastically, which are released to mitochondria to release energy. Studying lipid droplets under different chemical stimulations could help us understand the regulation of lipid droplets for metabolic disorders, such as obesity and diabetes.