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.

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.

Big data and machine learning for materials science.

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

A wearable sensor for the detection of sodium and potassium in human sweat during exercise.

The SwEatch platform, a wearable sensor for sampling and measuring the concentration of electrolytes in human sweat in real time, has been improved in order to allow the sensing of two analytes. The solid contact ion-sensitive electrodes (ISEs) for the detection of Na+ and K+ have been developed in two alternative formulations, containing either poly(3,4-ethylenedioxythiophene) (PEDOT) or poly(3-octylthiophene-2,5-diyl) (POT) as a conductive polymer transducing component. The solution-processable POT formulation simplifies the fabrication process, and sensor to sensor reproducibility has been improved via partial automation using an Opentron® automated pipetting robot. The resulting electrodes showed good sensitivity (52.4 ± 6.3 mV/decade (PEDOT) and 56.4 ± 2.2 mV/decade (POT) for Na+ ISEs, and 45.7 ± 7.4 mV/decade (PEDOT) and 54.3 ± 1.5 mV/decade (POT) for K+) and excellent selectivity towards potential interferents present in human sweat (H+, Na+, K+, Mg2+, Ca2+). The 3D printed SwEatch platform has been redesigned to incorporate a double, mirrored fluidic unit which is capable of drawing sweat from the skin through passive capillary action and bring it in contact with two independent electrodes. The potentiometric signal generated by the electrodes is measured by an integrated electronics board, digitised and transmitted via Bluetooth to a laptop. The results obtained from on-body trials on athletes during cycling show a relatively small increase in sodium (1.89 mM-2.97 mM) and potassium (3.31 mM-7.25 mM) concentrations during the exercise period of up to 90 min.

Direct Laser Writing of Four-Dimensional Structural Color Microactuators Using a Photonic Photoresist.

With the advent of direct laser writing using two-photon polymerization, the generation of high-resolution three-dimensional microstructures has increased dramatically. However, the development of stimuli-responsive photoresists to create four-dimensional (4D) microstructures remains a challenge. Herein, we present a supramolecular cholesteric liquid crystalline photonic photoresist for the fabrication of 4D photonic microactuators, such as pillars, flowers, and butterflies, with submicron resolution. These micron-sized features display structural color and shape changes triggered by a variation of humidity or temperature. These findings serve as a roadmap for the design and creation of high-resolution 4D photonic microactuators.

Smartphone based meat freshness detection.

In this work, a freshness colorimetric sensor has been integrated with pork meat packages. The sensor tracks rising CO2 levels in the package associated with meat spoilage, as CO2 levels increase with bacterial population. The color of the sensor changes depending on the quantity of bacteria present, therefore it can be correlated with the freshness of meat, in this case pork loin. Detection is achieved by a simple photograph using a smartphone, and analyzing the grey scale from the RGB space color with a custom made app. Only 2 µL of the cocktail (all components are nontoxic) is needed to prepare the sensor, which have been integrated inside meat packages using a variety of support materials prior to sealing. The Smartphone measurements have been validated using a reference method (Checkpoint Analyzer) and the results suggest it can provide the basis for a quick test of the quality of the packaged pork.

Fully automated, low-cost ion chromatography system for in-situ analysis of nitrite and nitrate in natural waters.

A cost-effective, automated and portable IC has been developed for in-situ analysis of nitrite and nitrate in natural waters. The system employed 3D printed pumps for eluent delivery and a deep-UV LED based optical detector. Isocratic separation and selective detection of nitrite and nitrate was achieved in under 3 min. The total weight of the analyser was ~11 kg, and included electronics along with a sample intake system for automated analysis. Linear calibration ranges were generated using different sample injection loops. Using a 150 µL loop, an analytical range (0.05-30 mg L-1 NO2-, 0.10-75 mg L-1 NO3-) suitable for freshwater analysis was generated, while using a 10 µL loop an analytical range (0.30-100 mg L-1 NO2-, 2.5-500 mg L-1 NO3-) suitable for effluent and domestic wastewater analysis was achieved. Chromatographic repeatability demonstrated by the system is graphically presented and RSD values of <4% were obtained in terms of peak area and retention time over 82 sequential runs. The system was deployed in-situ at multiple sites for varying deployment periods analysing septic tank water, effluent from a waste water treatment plant and stream water. The data generated by the in-situ system were comparable to grab sample data generated by accredited laboratory instrumentation.

3D Printed Sugar-Sensing Hydrogels.

The ability of boronic acids (BAs) to reversibly bind diols, such as sugars, has been widely studied in recent years. In solution, through the incorporation of additional fluorophores, the BA-sugar interaction can be monitored by changes in fluorescence. Ultimately, a practical realization of this technology requires a transition from solution-based methodologies. Herein, the first example of 3D-printed sugar-sensing hydrogels, achieved through the incorporation of a BA-fluorophore pair in a gelatin methacrylamide-based matrix is presented. Through optimization of monomeric cocktails, it is possible to use extrusion printing to generate structured porous hydrogels which show a measurable and reproducible linear fluorescence response to glucose and fructose up to 100 mm.

Integrated 3D printed heaters for microfluidic applications: Ammonium analysis within environmental water.

A multi-material 3D printed microfluidic reactor with integrated heating is presented, which was applied within a manifold for the colorimetric determination of ammonium in natural waters. Graphene doped polymer was used to provide localised heating when connected to a power source, achieving temperatures of up to 120 °C at 12 V, 0.7 A. An electrically insulating layer of acrylonitrile butadiene styrene (ABS) polymer or a new microdiamond-ABS polymer composite was used as a heater coating. The microdiamond polymer composite provided higher thermal conductivity and uniform heating of the serpentine microreactor which resulted in greater temperature control and accuracy in comparison to pure ABS polymer. The developed heater was then applied and demonstrated using a modified Berthelot reaction for ammonium analysis, in which the microreactor was configured at a predetermined optimised temperature. A 5-fold increase in reaction speed was observed compared to previously reported reaction rates. A simple flow injection analysis set up, comprising the microfluidic heater along with an LED-photodiode based optical detector, was assembled for ammonium analysis. Two river water samples and two blind ammonium standards were analysed and estimated concentrations were compared to concentrations determined using benchtop IC. The highest relative error observed following the analysis of the environmental samples was 11% and for the blind standards was 5%.

Fabrication of Rugged and Reliable Fluidic Chips for Autonomous Environmental Analyzers Using Combined Thermal and Pressure Bonding of Polymethyl Methacrylate Layers.

The fabrication of highly reliable and rugged fluidic chips designed for use in autonomous analyses for nutrient monitoring is described. The chips are based on a two-layer configuration with the fluidic channels produced in one layer using precision micromilling. The second capping layer contains through holes for sample/standard and reagent addition and waste removal post-analysis. Two optically clear polymethyl methacrylate (PMMA) windows are integrated into the opaque PMMA chip, orthogonal to a 22.5 mm-long section of the channel downstream from a serpentine reagent and sample/standard mixing region. An LED source is coupled into the channel through one of the windows, and the light intensity is monitored with a photodiode located at the distal end of the channel outside the second optically clear window. Efficient coupling of the source through the channel to the detector is achieved using custom-designed alignment units produced using 3D printing. In contrast to fluidic chips produced using solvent adhesion, the thermal-/pressure-bonded simplified method presented removes the need for surface treatment. Optimization of the thermal/pressure conditions leads to very strong adhesion between the PMMA layers, requiring forces in the region of 2000 N to separate them, which is necessary for the use in long-term deployments. Profilometry imaging shows minimal evidence of channel distortion after bonding. Finally, we show the potential of these techniques for environmental applications. The fluidic chips were integrated into prototype nutrient analyzers that display no evidence of leakage in extensive lab tests involving 2500 phosphate measurements using the yellow (vanadomolybdophosphoric acid) method. Similarly, excellent analytical performance (LOD is 0.09 µM) is reported for a 28-day field trial comprising 188 in situ autonomous phosphate measurements (564 measurements) in total including calibration.