Handheld Device for Selective Benzene Sensing over Toluene and Xylene

More than 1 million workers are exposed routinely to carcinogenic benzene, contained in various consumer products (e.g., gasoline, rubbers, and dyes) and released from combustion of organics (e.g., tobacco). Despite strict limits (e.g., 50 parts per billion (ppb) in the European Union), routine monitoring of benzene is rarely done since low-cost sensors lack accuracy. This work presents a compact, battery-driven device that detects benzene in gas mixtures with unprecedented selectivity (>200) over inorganics, ketones, aldehydes, alcohols, and even challenging toluene and xylene. This can be attributed to strong Lewis acid sites on a packed bed of catalytic WO3 nanoparticles that prescreen a chemoresistive Pd/SnO2 sensor. That way, benzene is detected down to 13 ppb with superior robustness to relative humidity (RH, 10–80%), fulfilling the strictest legal limits. As proof of concept, benzene is quantified in indoor air in good agreement (R2 ≥ 0.94) with mass spectrometry. This device is readily applicable for personal exposure assessment and can assist the implementation of low-emission zones for sustainable environments

Vision sensor with contrast, orientation and local motion extraction

Cette communication présente un capteur de vision qui extrait en temps réel l'amplitude des contrastes locaux de la scène observée, leur orientation et l'information de mouvement local, par un calcul analogique parallèle effectué dans le plan focal. La méthode d'extraction des caractéristiques précitées fournit une excellente stabilité de représentation sur toute la gamme dynamique. En outre, la sortie des informations est ordonnée sous forme de liste par ordre d'amplitude décroissante des caractéristiques, ce qui permet de construire des systèmes de traitement en temps réel extrêmement réactifs, car ne prenant en compte que l'information la plus pertinente. Un circuit fonctionnel comprenant une matrice de 128 x 128 pixels a été réalisé et caractérisé. Sa dynamique intra-scène est de 130dB et la précision de l'estimation est de 2% pour le contraste, de ± 3° pour l'orientation et de 3% pour le mouvement. La consommation totale est de 150mW en fonctionnement normal

Handheld Device for Selective Benzene Sensing over Toluene and Xylene

More than 1 million workers are exposed routinely to carcinogenic benzene, contained in various consumer products (e.g., gasoline, rubbers, and dyes) and released from combustion of organics (e.g., tobacco). Despite strict limits (e.g., 50 parts per billion (ppb) in the European Union), routine monitoring of benzene is rarely done since low-cost sensors lack accuracy. This work presents a compact, battery-driven device that detects benzene in gas mixtures with unprecedented selectivity (>200) over inorganics, ketones, aldehydes, alcohols, and even challenging toluene and xylene. This can be attributed to strong Lewis acid sites on a packed bed of catalytic WO3 nanoparticles that prescreen a chemoresistive Pd/SnO2 sensor. That way, benzene is detected down to 13 ppb with superior robustness to relative humidity (RH, 10-80%), fulfilling the strictest legal limits. As proof of concept, benzene is quantified in indoor air in good agreement (R-2 >= 0.94) with mass spectrometry. This device is readily applicable for personal exposure assessment and can assist the implementation of low-emission zones for sustainable environments.ISSN:2198-384

Molecular fingerprinting of biological nanoparticles with a label-free optofluidic platform

Abstract Label-free detection of multiple analytes in a high-throughput fashion has been one of the long-sought goals in biosensing applications. Yet, for all-optical approaches, interfacing state-of-the-art label-free techniques with microfluidics tools that can process small volumes of sample with high throughput, and with surface chemistry that grants analyte specificity, poses a critical challenge to date. Here, we introduce an optofluidic platform that brings together state-of-the-art digital holography with PDMS microfluidics by using supported lipid bilayers as a surface chemistry building block to integrate both technologies. Specifically, this platform fingerprints heterogeneous biological nanoparticle populations via a multiplexed label-free immunoaffinity assay with single particle sensitivity. First, we characterise the robustness and performance of the platform, and then apply it to profile four distinct ovarian cell-derived extracellular vesicle populations over a panel of surface protein biomarkers, thus developing a unique biomarker fingerprint for each cell line. We foresee that our approach will find many applications where routine and multiplexed characterisation of biological nanoparticles are required