Nanoimprinted, Submicrometric, MOF-Based 2D Photonic Structures: Toward Easy Selective Vapors Sensing by a Smartphone Camera

International audience2D photonic metal–oxide-framework-based homo- and hetero-structures are fabricated by soft lithographic approaches. As shown by A. Cattoni, M. Faustini and co-workers, these materials can be used as selective photonic sensing platforms. Detection of toxic vapors such as styrene are performed using an easy transduction method, compatible with smart-phone camera technologies

Ten years of lateral flow immunoassay technique applications: Trends, challenges and future perspectives

The Lateral Flow Immunoassay (LFIA) is by far one of the most successful analytical platforms to perform the on-site detection of target substances. LFIA can be considered as a sort of lab-in-a-hand and, together with other point-of-need tests, has represented a paradigm shift from sample-to-lab to lab-to-sample aiming to improve decision making and turnaround time. The features of LFIAs made them a very attractive tool in clinical diagnostic where they can improve patient care by enabling more prompt diagnosis and treatment decisions. The rapidity, simplicity, relative cost-effectiveness, and the possibility to be used by nonskilled personnel contributed to the wide acceptance of LFIAs. As a consequence, from the detection of molecules, organisms, and (bio)markers for clinical purposes, the LFIA application has been rapidly extended to other fields, including food and feed safety, veterinary medicine, environmental control, and many others. This review aims to provide readers with a 10-years overview of applications, outlining the trends for the main application fields and the relative compounded annual growth rates. Moreover, future perspectives and challenges are discussed

Crowd attributes estimation using support vector machine and deep learning with multi-source sensor fusion.

Thesis. M.E. American University of Beirut. Department of Mechanical Engineering, 2019. ET:6983.Advisor : Dr. Samir Mustapha, Assistant Professor, Mechanical Engineering ; Members of Committee : Dr. Zaher Dawy, Professor, Electrical and Computer Engineering ; Dr. Mohammad S. Harb, Assistant Professor, Mechanical Engineering.Includes bibliographical references (leaves 87-91)Unfortunate tragedies have previously been the result of high-density human crowds or pedestrian flow. In addition to, crowd behavior as a reaction to an incident aggravates the complexity and disruption of human flow, resulting in possible trampling and crushing situations. Therefore, it is important to monitor such crowd motion for danger warning and prevention. In this study, a frame work was established to provide continuous monitoring and estimation of crowd flow and load on pedestrian bridges, with particular focus on high crowd density enhancing operation safety. A main innovation under sensing instrumentation is the employment of structurally mounted Fiber Bragg Gratings (FBG) Fiber Optic Sensors (FOS), in conjunction with individually held wearable sensing devices incorporating Inertial Measurement Unit (IMU). Furthermore, the approach added innovation under machine learning employment, primarily Convolutional Neural Networks (CNN) along with conventional Support Vector Machine (SVM) algorithms thus generating crowd estimation models from gathered sensors’ data. The concept was validated using experimental measurements on two phases based on crowd replication scenarios on a scaled test bridge. Generated machine learning models demonstrated effectiveness in crowd attribute classification for flow activity and load characterization, along with regression model for load estimation. Multi-modal sensor fusion at the input and feature level was further applied on strain and acceleration data collected enriching the machine learning models, thus enhancing system efficiency and robustness against noisy and time shifted input data. The results showed that the monitoring solution to be highly effective with peak testing accuracy for single class flow activity classification at 98percent, multi-class flow and load characterization classification at 91percent, and percentage error for load estimation regression reaching a minimum of 9percent

New imidazole sensors synthesized for copper (II) detection in aqueous solutions

Water security, safety, availability and sustainability of water supply are increasingly problematic and difficult across the planet. Safe and rapid methods have been developed to purify water from cationic pollutants. New imidazole-derived fluorescent sensors, 2-(4,5-diphenyl-1-(p-tolyl)-1H-imidazol-2-yl) phenol (TS) and 2-(1-(4-methoxyphenyl)-4,5-diphenyl-1H-imidazol-2-yl) phenol (AS), have been synthesized and characterized, and their possibility as fluorescence sensors for copper (II) has been examined. In CH3CN/H2O (90 v/v), the TS and AS revealed a noticeable an electronic band at 320.00 nm and fluorescence band at 460.90 nm. The ratio metric alterations in the absorption and fluorescence spectra of TS and AS due to the dative covalent bond between them and the copper (II) were shown by the findings of copper (II) titration. The sensing mechanism was validated by optical investigations and FT-IR spectra. In mixed solvent solutions, the selectivity of TS and AS to copper (II) as fluorescent sensors has been demonstrated, with sensitivity as low as 0.09 and 0.28 μM, significantly less than the limit permitted via the US EPA for drinking water (2.00 μM). The identification limits of TS and AS were assessed to be 0.05 and 0.50 μM, respectively, using the spectrophotometric procedure. Stoichiometry binding between TS and AS with copper (II) was found to be 2:1 (tetrahedral structure) as indicated by Job\u27s method

New imidazole sensors synthesized for copper (II) detection in aqueous solutions

Water security, safety, availability and sustainability of water supply are increasingly problematic and difficult across the planet. Safe and rapid methods have been developed to purify water from cationic pollutants. New imidazole- derived fluorescent sensors, 2-(4,5-diphenyl-1-(p-tolyl)-1H-imidazol-2-yl) phenol (TS) and 2-(1-(4-methoxyphenyl)-4,5-diphenyl-1H-imidazol-2-yl) phenol (AS), have been synthesized and characterized, and their possibility as fluorescence sensors for copper (II) has been examined. In CH3CN/H2O (90 v/v), the TS and AS revealed a noticeable an electronic band at 320.00 nm and fluorescence band at460.90 nm. The ratio metric alterations in the absorption and fluorescence spectra of TS and AS due to the dative covalent bond between them and the copper (II) were shown by the findings of copper (II) titration. The sensing mechanism was validated by optical investigations and FT-IR spectra. In mixed solvent solutions, the selectivity of TS and AS to copper (II) as fluorescent sensors has been demonstrated, with sensitivity as low as 0.09 and 0.28 µM, significantly less than the limit permitted via the US EPA for drinking water (2.00 µM). The identification limits of TS and AS were assessed to be 0.05 and 0.50 µM, respectively, using the spectrophotometric procedure. Stoichiometry binding between TS and AS with copper (II) was found to be 2:1 (tetrahedral structure) asindicated by Job\u27s method

Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

New technique monitoring and transmission environmental data with mobile systems

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Data-Enabled Prediction Framework of Dynamic Characteristics of Rural Footbridges Using Novel Citizen Sensing Approach

Rural footbridges have proved to be an impetus for growth in vulnerable areas of the developing world, increasingly being built in many isolated communities around continents. Yet, little prior assessment of their dynamic characteristics had been made due to the non-traditional constraints that arise from instrumenting footbridges in rural, off-grid settings across multiple continents. Their characteristics remain largely unknown even if the low mass and flexible nature of rural footbridges make them vulnerable to wind-induced motions. To this end, this study proposes a data-enabled prediction framework based on a novel citizen sensing protocol, which aims at predicting the dynamic properties of rural footbridges during the conceptual design phase to enhance their safety under winds. The protocol is established which enables non-experts including local citizens in isolated communities to collect vibration data of rural footbridges by way of rapidly deployable and low-cost sensing systems in a novel application to full-scale monitoring with the concept of the community engagement. This citizen sensing data helps not only establish database with dynamic properties, but also develop empirical models to predict their dynamic properties of a footbridge in the conceptual design phase without detailed and bridge-specific dynamic modeling. In addition, a simple yet effective batch processing procedure to be done by non-experts is also devised to readily process upcoming citizen sensing data from new footbridges in the future, which offers instant and continuous updates of existing database with minimal efforts for enhancing the knowledge and the prediction of dynamic characteristics of rural footbridges

The technology development and commercialization of \u27smart\u27 scientific instrumentations

The technology development of two centrifuge-based instruments gave rise to an Internet-of-Things (IOT) biotech startup. The first instrument: the centrifuge force microscope (CFM) was recently introduced as a platform for massively parallel single-molecule manipulation and analysis. Here we developed a low-cost and self-contained CFM module that works directly within a commercial centrifuge, greatly improving accessibility and ease of use. Our instrument incorporates research grade video microscopy, a power source, a computer, and wireless transmission capability to simultaneously monitor many individually tethered microspheres. Building off of the first project, the second instrument we introduced was the “smart” centrifuge-test tube holder. The centrifuge is among the oldest and most widely used pieces of laboratory equipment, with significant applications that include clinical diagnostics and biomedical research. A major limitation of laboratory centrifuges is their ‘black box’ nature, limiting sample observation to before and after centrifugation. Thus, optimized protocols require significant trial and error, while unoptimized protocols waste time by centrifuging longer than necessary or material due to incomplete sedimentation. Here, we developed an instrumented centrifuge tube receptacle compatible with several commercial benchtop centrifuges that can provide real-time sample analysis during centrifugation. We demonstrated the system by monitoring cell separations during centrifugation for different spin speeds, concentrations, buffers, cell types, and temperatures. We show that the collected data are valuable for analytical purposes (e.g. quality control), or as feedback to the user or the instrument. For the latter, we verified an adaptation where complete sedimentation turned off the centrifuge and notified the user by a text message. Our system adds new functionality to existing laboratory centrifuges, saving users time and providing useful feedback. This add-on potentially enables new analytical applications for an instrument that has remained largely unchanged for decades. The accomplishment of the technology developments from the CFM and the “smart” test-tube holder lead to the realization that there is a strong demand within the scientific community for ‘smart’ scientific add-ons that complement existing instruments to improve efficiency and work-flow. The acknowledgement that scientists are forced to perform and wait for mundane experiments to be completed resulted in the formation of one of the first industrial-IOT (Internet of Things) biotech startup, Advanced Modular Instruments (AMI). The biotech startup develops next generation digital, “smart”, internet-connected scientific instruments to scientific discovery. The “smart” scientific instruments can be controlled through a smartphone app, tablet app and web UI (user interface). The information from “smart” scientific instruments to the remote computing servers are encrypted and obey compliance regulations to insure the integrity of the sensor readings for commercial, private and government use

Hybrid point-of-care devices for visual detection of biomarkers and drugs

Early diagnostics is a crucial part of clinical practice offering a rapid and convenient way to investigate and quantify the presence of key biomarkers related to specific pathologies and increasing the chance of successful treatments. In this regard, point-of-care testing (POCT) shows several advantages enabling simple and rapid analyses, allowing for real-time results, and permitting home testing. Metallic nanoparticles (NPs), like gold NPs (AuNPs), can be beneficially integrated into POC devices thanks to their tunable plasmonic properties which provide a naked-eye read-out. Moreover, the high sensitivity of NPs enables the detection of biomarkers in non-invasive fluids where the concentrations are typically low. These biofluids, like saliva and urine, are functionally equivalent to serum in reflecting the physiological state of the body, whilst they are easier to handle, collect, and store. In this thesis, I first reported the design and development of a colorimetric strategy based on the morphological change of multibranched plasmonic AuNPs, aimed at detecting glucose in saliva. The sensing approach relied on a target-induced reshaping process which involves the oxidation of the NP tips and the transformation into a spherical shape, characterized by a naked-eye detectable blue-to-pink color change. The platform proved to be beneficial in the early and non-invasive diagnosis of hyperglycemia. The successful technological transfer on a solid substrate paved the way for the realization of a dipstick prototype for home testing. Then, the strategy was adapted to other biomarkers, leading to the development of a multiplexing test for the simultaneous detection of three salivary analytes (cholesterol, glucose, and lactate). This multiplexing assay enabled to save reagents, costs, and time, whilst increasing the overall clinical value of the test. Exploiting the microfluidics applied on a paper sheet, I realized a monolithic and fully integrated POC device, through a low-cost and fast CO2 laser cutter. The platform showed excellent selectivity and multiplexing ability, with negligible interferences. The second part of my thesis was focused on the development of POC devices for the detection of anticancer drug contaminations in water solutions and urine samples. Antiblastic agents have revealed high toxicity for the exposed healthcare workers who prepare and administer these drugs in occupational environments. Hence, continuous monitoring is highly required, and POCT shows tremendous potential in this context. With this aim, I realized a lateral-flow (LF) device for the assessment of doxorubicin contamination, using the fluorescent properties of the drug for naked-eye detection. The pharmacological recognition of the DNA probe was exploited to overcome the lack of anti-doxorubicin antibodies. The highly sensitive strategy was successfully adapted to a real urine sample, without resorting to complex pretreatment procedures. Then, I developed a competitive LF device for the detection of methotrexate (MTX). AuNPs were employed as the label molecules and the pharmacological competition of folic acid and MTX for the capture enzyme was exploited as the recognition mechanism, instead of costly antibodies. Despite the sensitivity requires further improvements, the strategy showed fast and reliable results, demonstrating a high potential for workers’ safety control