Critical importance of RGB color space specificity for colorimetric bio/chemical sensing: A comprehensive study

The use of the RGB color model in colorimetric chemical sensing via imaging techniques is widely prevalent in the literature. However, the lack of specificity in the selection of RGB color space during capture and analysis presents a significant challenge in creating standardised methods for this field and possible discrepancies. In this study, we conducted a comprehensive comparison and contrast of a total of 68 RGB color spaces to evaluate their respective impacts on colorimetric bio/chemical sensing. We explore the impact of dynamic range, sensitivity, and limit of detection, and show that the lack of specificity in RGB color space selection can significantly impact colorimetric chemical sensing by 42-77%. We also explore the impact of underlying RGB comparisons and demonstrate a further 18.3% discrepancy between RGB color spaces. By emphasising the importance of proper RGB color space selection and handling, our findings contribute to a better understanding of this critical area and present valuable opportunities for future research. We further provide valuable insights for creating standardised methods in this field, which can be utilised to avoid discrepancies and ensure accurate and reliable analysis in colorimetric bio/chemical sensing.

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks

This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8-99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.

Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device's performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management.

LED PEDD Discharge Photometry: Effects of Software Driven Measurements for Sensing Applications

This work explores the effects of embedded software-driven measurements on a sensory target when using a LED as a photodetector. Water turbidity is used as the sensory target in this study to explore these effects using a practical and important water quality parameter. Impacts on turbidity measurements are examined by adopting the Paired Emitter Detector Diode (PEDD) capacitive discharge technique and comparing common embedded software/firmware implementations. The findings show that the chosen software method can (a) affect the detection performance by up to 67%, (b) result in a variable sampling frequency/period, and (c) lead to an disagreement of the photo capacitance by up to 23%. Optimized code is offered to correct for these issues and its effectiveness is shown through comparative analyses, with the disagreement reduced significantly from 23% to 0.18%. Overall, this work demonstrates that the embedded software is a key and critical factor for PEDD capacitive discharge measurements and must be considered carefully for future measurements in sensor related studies.

Advances in Optical Based Turbidity Sensing Using LED Photometry (PEDD)

Turbidity is one of the primary metrics to determine water quality in terms of health and environmental concerns, however analysis typically takes place in centralized facilities, with samples periodically collected and transported there. Large scale autonomous deployments (WSNs) are impeded by both initial and per measurement costs. In this study we employ a Paired Emitter-Detector Diode (PEDD) technique to quantitatively measure turbidity using analytical grade calibration standards. Our PEDD approach compares favorably against more conventional photodiode-LED arrangements in terms of spectral sensitivity, cost, power use, sensitivity, limit of detection, and physical arrangement as per the ISO 7027 turbidity sensing standard. The findings show that the PEDD technique was superior in all aforementioned aspects. It is therefore more ideal for low-cost, low-power, IoT deployed sensors. The significance of these findings can lead to environmental deployments that greatly lower the device and per-measurement costs.

Fabrication of Aligned Biomimetic Gellan Gum-Chitosan Microstructures through 3D Printed Microfluidic Channels and Multiple In Situ Cross-Linking Mechanisms

In this study we use a combination of ionic- and photo-cross-linking to develop a fabrication method for producing biocompatible microstructures using a methacrylated gellan gum (a polyanion) and chitosan (a polycation) in addition to lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) as the photoinitiator. This work involves the development of a low-cost, portable 3D bioprinter and a customized extrusion mechanism for controlled introduction of the materials through a 3D printed microfluidic nozzle, before being cross-linked in situ to form robust microstructure bundles. The formed microstructures yielded a diameter of less than 1 μm and a tensile strength range of ∼1 MPa. This study is the first to explore and achieve GGMA:CHT microstructure fabrication by means of controlled in-line compaction and photo-cross-linking through 3D printed microfluidic channels.

Patterning and process parameter effects in 3D suspension near-field electrospinning of nanoarrays

The extracellular matrix (ECM) contains nanofibrous proteins and proteoglycans. Nanofabrication methods have received growing interest in recent years as a means of recapitulating these elements within the ECM. Near-field electrospinning (NFES) is a versatile fibre deposition method, capable of layer-by-layer nano-fabrication. The maximum layer height is generally limited in layer-by-layer NFES as a consequence of electrostatic effects of the polymer at the surface, due to residual charge and polymer dielectric properties. This restricts the total volume achievable by layer-by-layer techniques. Surpassing this restriction presents a complex challenge, leading to research innovations aimed at increasing patterning precision, and achieving a translation from 2D to 3D additive nanofabrication. Here we investigated a means of achieving this translation through the use of 3D electrode substrates. This was addressed by in-house developed technology in which selective laser melt manufactured standing pillar electrodes were combined with a direct suspension near-field electrospinning (SNFES) technique, which implements an automated platform to manoeuvre the pillar electrodes around the emitter in order to suspend fibres in the free space between the electrode support structures. In this study SNFES was used in multiple operation modes, investigating the effects of varying process parameters, as well as pattern variations on the suspended nanoarrays. Image analysis of the nanoarrays allowed for the assessment of fibre directionality, isotropy, and diameter; identifying optimal settings to generate fibres for tissue engineering applications.

Quantitative ultrasound imaging of cell-laden hydrogels and printed constructs

In the present work we have revisited the application of quantitative ultrasound imaging (QUI) to cellular hydrogels, by using the reference phantom method (RPM) in combination with a local attenuation compensation algorithm. The investigated biological samples consisted of cell-laden collagen hydrogels with PC12 neural cells. These cell-laden hydrogels were used to calibrate the integrated backscattering coefficient (IBC) as a function of cell density, which was then used to generate parametric images of local cell density. The image resolution used for QUI and its impact on the relative IBC error was also investigated. Another important contribution of our work was the monitoring of PC12 cell proliferation. The cell number estimates obtained via the calibrated IBC compared well with data obtained using a conventional quantitative method, the MTS assay. Evaluation of spectral changes as a function of culture time also provided additional information on the cell cluster size, which was found to be in close agreement with that observed by microscopy. Last but not least, we also applied QUI on a 3D printed cellular construct in order to illustrate its capabilities for the evaluation of bioprinted structures. STATEMENT OF SIGNIFICANCE: While there is intensive research in the areas of polymer science, biology, and 3D bio-printing, there exists a gap in available characterisation tools for the non-destructive inspection of biological constructs in the three-dimensional domain, on the macroscopic scale, and with fast data acquisition times. Quantitative ultrasound imaging is a suitable characterization technique for providing essential information on the development of tissue engineered constructs. These results provide a detailed and comprehensive guide on the capabilities and limitations of the technique.

A direct 3D suspension near-field electrospinning technique for the fabrication of polymer nanoarrays

Near-field electrospinning (NFES) is widely recognized as a versatile nanofabrication method, one suitable for applications in tissue engineering. Rapid developments in this field have given rise to layered nanofibrous scaffolds. However, this electrostatic fabrication process is limited by the electric field inhibitory effects of polymer deposition. This leads to a major challenge: how to surpass this limitation on planar/layered constructs. While the current focus in this area largely lies with the investigation of new materials, techniques and increasing precision of NFES systems and patterning, exploration of complex collector substrates is often restricted by (i) available technology and (ii) access to complex electrode manufacturing tools. To achieve nanofiber arrays suspended in free space, this paper documents both the development of an integrated NFES system and the potential of standing electrodes manufactured via selective laser melting. This system was first tested by 2D patterning on planar silicon, using polyethylene oxide polymer solution. To demonstrate suspension NFES, two patterns operating within and around the standing electrodes produced high volume suspended nanoarrays. Image analysis of the arrays allowed for the assessment of fiber directionality and isotropy. By scanning electron microscopy, it was found that a mean fiber diameter of 310 nm of the arrays was achieved. Effectively manoeuvring between the electrode pillars required a precision automated system (unavailable off-the-shelf), developed in-house. This technique can be applied to the fabrication of nanofiber structures of sufficient volume for tissue engineering.

Understanding the loss mechanisms in high-performance solution-processed small molecule bulk heterojunction solar cells doped with a PFN impurity

A small amount of PFN, introduced as an impurity, can have a dramatic impact on the performance of small molecule-based BHJ solar cells.

A contactless approach for monitoring the mechanical properties of swollen hydrogels

Using a customized ultrasound setup we investigate the feasibility of using a contactless approach to study the bulk mechanical properties of swollen hydrogels. The study involved two different hydrogels, gelatin methacrylate (GelMa) and green algae extract methacrylate (GAEM), which were prepared to provide materials with varying modulus and different swelling properties. Two approaches have been developed. In the first case, ultrasound was compared to Young's modulus measured by indentation. It was found that can be linearly related to indentation modulus values only when the hydrogel swelling ratio is taken into account. In the second approach, an exponential dependency between swelled thickness and indentation modulus was found. This is representative for each hydrogel and purification method in addition to being independent of the conditions used within the toughness range explored. The results of this study indicate that a simple thickness measurement via the proposed approach can provide a direct relationship to Young's modulus upon calibration.