OptiJ: Open-source optical projection tomography of large organ samples

The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 µm resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples

OptiJ: Open-source optical projection tomography of large organ samples

Abstract: The three-dimensional imaging of mesoscopic samples with Optical Projection Tomography (OPT) has become a powerful tool for biomedical phenotyping studies. OPT uses visible light to visualize the 3D morphology of large transparent samples. To enable a wider application of OPT, we present OptiJ, a low-cost, fully open-source OPT system capable of imaging large transparent specimens up to 13 mm tall and 8 mm deep with 50 µm resolution. OptiJ is based on off-the-shelf, easy-to-assemble optical components and an ImageJ plugin library for OPT data reconstruction. The software includes novel correction routines for uneven illumination and sample jitter in addition to CPU/GPU accelerated reconstruction for large datasets. We demonstrate the use of OptiJ to image and reconstruct cleared lung lobes from adult mice. We provide a detailed set of instructions to set up and use the OptiJ framework. Our hardware and software design are modular and easy to implement, allowing for further open microscopy developments for imaging large organ samples

Portable and Non-Intrusive Sensors for Monitoring Air Pollution

Air pollution is a global problem. Particulate Matter (PM) of aerodynamic diameter smaller than 2.5 μm (known as PM₂.₅) and NO₂ are important classes of pollutants because of their size and emission sources and potential effects of exposure beyond 25 μm/m³ and 40 μm/m³ annual mean respectively. This thesis presents work that has been done to develop new and miniaturized/non intrusive ($300), size (smallest ones are several tens of cm³ in volume) and the accuracy (±10%) of the sensors that motivated the design, simulation and subsequent fabrication of the miniaturized device. It is shown that the capacitive-based sensor is easily miniaturizable and has sensitivity to single particles flowing at a distance of up to 18 μm above the electrode surface. This new sensor concept and its simulated multiphysics model is unique because it uses thermophoresis to separate particles of PM₂.₅ and PM₁₀ from a single airflow. For the NO₂ sensors, the availability of selective sensors that function in humid environments is a major need. Further, both sensor types need to be robust against interferent species and environmental variations. In this thesis, I present chemiresistors based on graphene/carboxymethyl cellulose (CMC) and carbon nanotube/CMC composites capable of sensing low, down to 20 ppm and 6 ppm, NO₂ concentration respectively. The new sensors show selectivity to NO₂ because of the selective oxidation of the composite component CMC salt by NO₂. Due to the Solubility of CMC in water and response of the sensor to ppm-level NO₂, a washable textile-based NO₂ sensor based on a reduced graphene oxide/MoS₂ composite material was developed. The sensor has selectivity to NO₂ and can detect ultra-low (100 ppb) NO₂ concentration levels in >60% humid air. It can also detect down to 20 ppb NO₂ in dry air. The next objective, beyond the scope of this work, is to integrate both PM₂.₅ and NO₂ detection and monitoring. Commercial exploitation of the technologies developed is now being explored through a University spin-out.Federal Government of Nigeria through the Presidential Special Scholarship for Innovation and Development (PRESSID) administered by the National Universities Commission (NUC) and funded by Petroleum Technology Development Fund (PTDF) Engineering and Physical Sciences Research Council (EPSRC) through the Sensors CDT

Particulate Matter Monitoring: Past, Present and Future

The health problems caused by exposure to airborne Particulate Matter (PM) beyond safe limits have been studied for many years. Government regulatory agencies have adapted and updated the safe exposure limits as more progress is made both in policy developments and detection system design. Bulky PM detectors, though very accurate do not provide sufficient spatial and temporal resolution, and are static and expensive. Current much smaller commercial PM sensors are mobile but still mostly too expensive and largely still too big for real-time continuous personal use. They also must be calibrated to convert their counts to mass concentration despite their variation from unit to unit. The continuous drive towards having a cheaper, smaller, yet more effective PM sensors for personal exposure analysis and indoor environments is pushing the current boundaries of current techniques. Emerging PM sensing techniques must now achieve this, while also linking to other structured source apportionment and semantic analysis of air quality data aimed at providing useful information about user activities mostly provided via the internet. This review highlights research on PM detection and monitoring, covering methods and principle of operation of detection instruments, emerging trends and future outlooks. Further, this work reviews PM detection challenges, measurement interpretation and possible solutions going forward

Research data supporting "Unencapsulated and Washable Two-Dimensional Material Electronic-Textile for NO2 Sensing In Ambient Air"

Dataset related to the manuscript entitled "Unencapsulated and Washable Two-Dimensional Material Electronic-Textile for NO2 Sensing In Ambient Air". Abstract: Materials adopted in electronic gas sensors, such as chemiresistive-based NO2 sensors, for integration in clothing fail to survive standard wash cycles due to the combined effect of aggressive chemicals in washing liquids and mechanical abrasion. Device failure can be mitigated by using encapsulation materials, which, however, reduces the sensor performance in terms of sensitivity, selectivity, and therefore utility. A highly sensitive NO2 electronic textile (e-textile) sensor was fabricated on Nylon fabric, which is resistant to standard washing cycles, by coating Graphene Oxide (GO), and GO/Molybdenum disulfide (GO/MoS2) and carrying out in situ reduction of the GO to Reduced Graphene Oxide (RGO). The GO/MoS2 e-textile was selective to NO2 and showed sensitivity to 20 ppb NO2 in dry air (0.05%/ppb) and 100 ppb NO2 in humid air (60% RH) with a limit of detection (LOD) of ~ 7.3 ppb. The selectivity and low LOD is achieved with the sensor operating at ambient temperatures (~ 20 °C). The sensor maintained its functionality after undergoing 100 cycles of standardised washing with no encapsulation. The relationship between temperature, humidity and sensor response was investigated. The e-textile sensor was embedded with a microcontroller system, enabling wireless transmission of the measurement data to a mobile phone. These results show the potential for integrating air quality sensors on washable clothing for high spatial resolution (< 25 cm2)—on-body personal exposure monitoring.Innovate UK project No. 103543 MPSENS, EPSRC grants EP/KO3099X/1 and EP/LO15889/1, European H2020 project 1D-NEON (Grant Agreement No. 685758

Diffusive drug delivery in the brain extracellular space from a cellular scale microtube.

UNLABELLED: The effectiveness of state-of-the-art systemic treatments for neurological disorders is hampered not only by the difficulty in crossing the blood brain barrier but also off-target drug interactions. In this study, a delivery method is simulated for a novel U-shaped microtube locally infusing drugs directly into the extracellular space of the brain and relying on diffusion as a transport mechanism. The influence of flow rate, drug properties and device geometry are investigated. It is anticipated that these findings will accelerate progress on both developmental and applied drug delivery and materials research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1557/s43579-022-00247-9

Fluidic enabled bioelectronic implants: opportunities and challenges.

Bioelectronic implants are increasingly facilitating novel strategies for clinical diagnosis and treatment. The integration of fluidic technologies into such implants enables new complementary routes for sensing and therapy alongside electrical interaction. Indeed, these two technologies, electrical and fluidic, can work synergistically in a bioelectronics implant towards the fabrication of a complete therapeutic platform. In this perspective article, the leading applications of fluidic enabled bioelectronic implants are highlighted and methods of operation and material choices are discussed. Furthermore, a forward-looking perspective is offered on emerging opportunities as well as critical materials and technological challenges

Pathogen Detection via Impedance Spectroscopy-Based Biosensor.

Peer reviewed: TruePublication status: PublishedThis paper presents the development of a miniaturized sensor device for selective detection of pathogens, specifically Influenza A Influenza virus, as an enveloped virus is relatively vulnerable to damaging environmental impacts. In consideration of environmental factors such as humidity and temperature, this particular pathogen proves to be an ideal choice for our study. It falls into the category of pathogens that pose greater challenges due to their susceptibility. An impedance biosensor was integrated into an existing platform and effectively separated and detected high concentrations of airborne pathogens. Bio-functionalized hydrogel-based detectors were utilized to analyze virus-containing particles. The sensor device demonstrated high sensitivity and specificity when exposed to varying concentrations of Influenza A virus ranging from 0.5 to 50 μg/mL. The sensitivity of the device for a 0.5 μg/mL analyte concentration was measured to be 695 Ω· mL/μg. Integration of this pathogen detector into a compact-design air quality monitoring device could foster the advancement of personal exposure monitoring applications. The proposed sensor device offers a promising approach for real-time pathogen detection in complex environmental settings