Individual Gas Research Articles

Comparative technoeconomic analysis of centralised and decentralised water source heat pump systems using CATHeaPS.

In the efforts to decarbonise the heat sector, heat pumps can offer a cost-effective transition away from fossil fuels. Water Source Heat Pumps (WSHP) can be utilised in cases where ambient water sources (river, ground water, abandoned mines water) are present. However, the economic benefits of different levels of heat pump centralisation as well as their comparative advantages over other decentralised options such as individual Air Source Heat Pumps (ASHP) or Gas Boilers (GB) remain uncertain and further investigations are necessary to fully assess their potential. This study introduces CATHeaPS, a Centralisation Analysis Tool for Heat Pump Systems, a user-friendly open access modelling tool that enables the technoeconomic assessment of (a) district heating networks with a centralised WSHP, and (b) ambient networks with decentralised building level WSHPs against individual ASHPs and GB for a range of consumer classes. CATHeaPS provides a complete project cashflow for each supply option and is verified against published data and outputs from a UK industrial case study, with slightly altered data to ensure confidentiality. A data analysis highlights the break-even points for the number of residential properties, beyond which centralised solutions are more economic than decentralised energy supply options for different housing densities. A thorough sensitivity analysis is also conducted to identify the impact of different input parameters on the levelised cost of energy of each supply option. It is found that discount rate has the largest impact for both networks, followed by CAPEX and energy costs. This study aims to help stakeholders and decision makers in two ways. It introduces a novel, easy-to-use open access technoeconomic tool that enables a high-level analysis of energy, hydraulic and economic factors for any project area. Furthermore, it maps the boundaries of beneficial operation for different levels of centralisation for residential consumers and gives preliminary suggestions on which energy supply option is better suited to a given project.

Monitoring of greenhouse gas emissions and compost quality during olive mill waste co-composting at industrial scale: The effect of N and C sources

Olive mill wastes (OMW) management by composting allows to obtain valuable fertilizing products, but also implies significant fluxes of greenhouse gases (GHG). For a proper OMW composting, high C- and N co-substrates are necessary, but little is known concerning their effect on GHG emissions in OMW-industrial scale composting. In this study, different co-composting agents (cattle manure (CM), poultry manure (PM), sheep manure (SM) and pig slurry solid fraction (PSSF) as N sources and olive leaves (OLW) and urban pruning residues (UPR) as bulking agents and C sources) were used for OMW composting at industrial scale. Physico-chemical and chemical properties in the composting samples, and GHG (CO2, CH4 and N2O) fluxes were monitored in 12 industrial-scale windrows. GHG emissions were firstly influenced by N source, with the highest accumulated global warming potential (GWP) associated with PM (512 kg CO2eq pile-1), since PM composts were associated with the greatest N2O (0.33 kg pile-1) and CH4 emissions (15.67 kg pile-1). Meanwhile, PSSF was associated with the highest CO2 emissions (1113 kg pile-1). UPR as a bulking agent facilitated 10 % greater mineralization of the biomass than OLW, however this C-source was not associated with higher GHG emissions. The results showed that while mineralization dynamics may be impacted by C sources, GHG emissions were mainly conditioned by the characteristics of nutrient-heavy feedstocks (PM and SM). Moreover, manures as nitrogen-laden co-substrates had widely differing effects on total GWP, and that of individual gases, but further research is necessary to understand the mechanisms explaining such differences.

Holistic method for determining the techno-economic feasibility of waste heat for the planning of the low-temperature district heating systems

The heating sector is the most energy-intensive sectors, accounting for almost 50% of final energy consumption at the EU and almost 70% of that energy comes from fossil fuels. Urban waste heat sources, the refrigeration system of supermarkets, cooling systems of supermarkets, shopping malls, data centres, and power substations are known as systems that can be heat sources for the district heating (DH) system. This paper presents a holistic method to assess the economic, energy, and environmental benefits of integrating urban waste heat sources into DH. The method considers different boundary conditions and compares these systems to individual natural gas boilers, as well as to DH based on natural gas boilers. The observed boundary conditions include varying temperature regimes of the DH network, heat demands, different shares of space heating, and plot ratios. The method has been implemented and tested in the case of Zagreb. The results showed that Low-Temperature DH is viable for lower plot ratio and high heat demand, while Neutral-temperature DH are suitable for high plot ratio and high heat demand. Ultra Low-Temperature DH is viable for medium plot ratio and medium heat demand. The conventional solutions remain viable for low plot ratio and low heat demand.

An integrated dynamic modeling workflow for acid gas and CO2 geologic storage screening in saline aquifers with faults: A case study in Western Canada

This study investigates the feasibility of storing the acid gas produced from the oil and gas facilities in Southern Saskatchewan into the Basal Sand aquifer using a coupled wellbore-aquifer-compositional reservoir model. The simulations investigate the pressure change around the fault in proximity of the primary storage location incorporating the influence of reservoir permeability, fault transmissibility, and wellbore configuration, on the factors critical to safe and efficient storage, such as plume migration, pressure changes, and CO2 storage capacity. A compositional fluid model created using an equation of state was integrated into the reservoir model. Simultaneous incorporation of fault transmissibility, phase solubility, water salinity, temporal in-situ hysteresis and structural trapping, and in-situ compositional tracking of individual gas components is considered as the main novelty of this work. The main challenge of the study was the lack of available data to characterize the aquifer. To this end, a comprehensive workflow of reservoir studies and modeling was applied to reduce the uncertainties and evaluate the site selection. The Basal sand scoping models reveal that the aquifer is expected to handle the required disposal volume given its extent. The injected acid gas plume migrates laterally and preferentially towards the northwest, away from the fault, owing to the aquifer's geological structure. CO2 remains entirely in the supercritical state, offering storage advantages due to its lower volume. The reservoir permeability significantly impacts the pressure patterns with lower permeability formations triggering higher wellhead injection pressures. Substantial pressure increases around the sealing fault can be observed. Pressure changes of 110 kPa (16 psi) to over 400 kPa (58 psi) were observed at the fault segment after 20 years of continuous gas injection for the expected range of reservoir properties. Mitigation strategies to minimize the increase in fault pressure entail relocating the injection site away from the fault or utilizing a horizontal well trajectory and using an observation well near the fault for monitoring any pressure buildup and slippage.

144 Assessing economic implications of thermal stress of Bos indicus and Bos taurus species during finishing in northern Colorado

Abstract The objective of this study was to determine the economic implications of thermal stress on climate adaptive capacity, resiliency, and impact of Bos indicus and Bos taurus finishing steers using common finishing systems. The experimental design was a completely randomized design, with a 2 by 2 factorial arrangement of finishing system (‘natural’ or ‘conventional’) and species of cattle (Bos indicus or Bos taurus). Steers [n = 200; body weight (BW) ~450 kg] were housed in 20 feedlot pens (10 steers/pen) for the first 84 d, where cattle were blocked by initial BW. For the remainder of the 180 d (96 d) finishing period, cattle were moved to four Climate Smart Research Pens (50 steers/pen, 1 pen/ treatment, and species combination). Within each Climate Smart Research Pen, five SmartFeed systems were used to evaluate individual animal feed intake, one GreenFeed emission measurement system was used to evaluate individual animal gas emissions flux of enteric CH4, O2, H2, and CO2, and were equipped for nutrient mass balance of carbon, nitrogen, and phosphorus. Animal BW were collected every 28 d for analysis of average daily gain (ADG) and feed efficiency. Thermal stress behaviors were evaluated by one trained personnel on a subset of 7 steers/treatment via the measurement of respiration rate, open mouth breathing, rumen temperature, number of daily water intake events, lying and standing behavior, and pen surface temperature for the last 96 d of the finishing period. At harvest, carcass characteristics, liver scores, and heart scores were collected. Economic analyses were conducted post-hoc to determine the profitability of each species based on economic implications, tradeoffs, carcass quality and quantity, and time to finish. Selection of cattle for climate adaptive features may involve production tradeoffs (e.g., depressed rate of gain, greater yield risk, lower technical efficiency) and adjustment costs (e.g., re-optimizing feed for a new breed), which affect the adoption of less common breeds into the US cattle supply chain. Adding to the complexity of this evaluation, consumers preferences may not adapt quickly, affecting the prospect of Bos indicus cuts of meat in the market. We also, therefore, explore the supply- and demand-side effects of climate change adaptation through breed selection in the beef industry.

Gas Evolution in Water Electrolysis.

Gas bubbles generated by the hydrogen evolution reaction and oxygen evolution reaction during water electrolysis influence the energy conversion efficiency of hydrogen production. Here, we survey what is known about the interaction of gas bubbles and electrode surfaces and the influence of gas evolution on practicable devices used for water electrolysis. We outline the physical processes occurring during the life cycle of a bubble, summarize techniques used to characterize gas evolution phenomena in situ and in practical device environments, and discuss ways that electrodes can be tailored to facilitate gas removal at high current densities. Lastly, we review efforts to model the behavior of individual gas bubbles and multiphase flows produced at gas-evolving electrodes. We conclude our review with a short summary of outstanding questions that could be answered by future efforts to characterize gas evolution in electrochemical device environments or by improved simulations of multiphase flows.

Computational fluid dynamics modeling of gas bubble separation efficiency in downhole separators and vertical / inclined annulus: An experimental validation and analysis

This study presents a comprehensive investigation into gas bubble separation efficiency in a vertical downhole separator using computational fluid dynamics (CFD) modeling, which is validated with experimental data. The research aims to provide detailed insights into the separation mechanisms and the factors affecting them, such as liquid velocity, gas bubble sizes, and positions. Using an Euler-Lagrange multiphase model, the behavior of individual gas bubbles within the liquid-gas mixture was simulated. The results demonstrate that separation efficiency decreases as the liquid flow rate increases. This reduction in efficiency occurs because the increased liquid flow rate drags more bubbles within the stream, making it harder for them to separate. The separation process is primarily driven by the buoyancy force acting on the bubbles and the velocity profile of the liquid flow. Smaller bubbles, which tend to be near the casing wall, separate more efficiently, while larger bubbles are predominantly located between the second and third quarters of the annulus space. The "quarters" refer to radial divisions from the casing wall to the tubing wall, with the first quarter closest to the casing wall and the fourth quarter closest to the tubing wall. The study introduces a nonlinear regression model based on the numerical results to predict liquid slug separation efficiency. This model, validated against experimental data, accurately predicts separation efficiency and offers a robust methodology for estimating the efficiency of gravity-driven gas-liquid separators. This methodology is crucial for refining separator design and improving the operational efficiency of downhole separation systems, which are vital for enhancing the performance of pumping systems in oil and gas wells. The contributions of this study include a deeper understanding of the downhole separation process, the development of a simplified model for assessing bubble separation efficiency, and the potential application of the proposed methodology to different gas-liquid separator designs and inclination angles, thereby informing the design and operation of downhole separation systems.

Transitioning Work Arrangements in the Construction Industry: Changes in Time-Use Patterns and Individual Greenhouse Gas Emissions

Transitioning Work Arrangements in the Construction Industry: Changes in Time-Use Patterns and Individual Greenhouse Gas Emissions

Pressure Gain Combustion for Gas Turbines: Analysis of a Fully Coupled Engine Model

Abstract The ‘Shockless Explosion Combustion’ (SEC) concept for gas turbine combustors, introduced in 2014, approximates constant volume combustion (CVC) by harnessing acoustic confinement of autoigniting gas packets. The resulting pressure waves simultaneously transmit combustion energy to a turbine plenum and facilitate the combustor's recharging against an average pressure gain. Challenges in actualizing an SEC-driven gas turbine include i) the creation of charge stratifications for nearly homogeneous autoignition, ii) protecting the turbo components from combustion-induced pressure fluctuations, iii) providing evidence that efficiency gains comparable to those of CVC over deflagrative combustion can be realized, and iv) designing an effective one-way intake valve. This work addresses challenges i)-iii) utilizing computational engine models incorporating a quasi-one-dimensional combustor, zero- and two-dimensional compressor and turbine plena, and quasi-stationary turbo components. Two SEC operational modes are identified which fire at roughly one and two times the combustor's acoustic frequencies. Results for SEC-driven gas turbines with compressor pressure ratios of 6:1 and 20:1 reveal 1.5-fold mean pressure gains across the combustors. Assuming ideally efficient compressors and turbines, efficiency gains over engines with deflagration-based combustors of 30% and 18% are realized, respectively. With absolute values of 52% and 66%, the obtained efficiencies are close to the theoretical Humphrey cycle efficiencies of 54% and 65% for the mentioned pre-compression ratios. Detailed thermodynamic cycle analyses for individual gas parcels suggest that there is room for further efficiency gains through optimized plenum and combustor designs.

Neural network-assisted emission spectra analysis of gases using MEMS sensor: Predicting chemical composition and pressure

The paper explores the potential use of neural networks to predict the chemical composition as well as the total and partial pressure of individual gases in the mixture based on their emission spectra. Excitation/ionization of gas particles is ensured by a miniature MEMS (micro-electro-mechanical) sensor. The impact of measurement conditions on the generated spectra and possible challenges in analysis are addressed. The trained neural networks achieved a mean absolute error of approximately 1.4% in determining the chemical composition of a mixture of methane and hydrogen and 0.8% for a mixture of nitrogen and oxygen. For pressure prediction, spanning several orders of magnitude, a general model predicting pressure for ten different gases achieved a relative mean absolute error of 4.5%. Additionally, a model for gas classification achieved an accuracy of over 99%.

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Prethermalization in coupled one-dimensional quantum gases

We consider the problem of the development of steady states in one-dimensional Bose gas tubes that are weakly coupled to one another through a density-density interaction. We analyze this development through a Boltzmann collision integral approach. We argue that when the leading order of the collision integral, where single particle-hole excitations are created in individual gases, is dominant, the state of the gas evolves first to a non-thermal fixed point, i.e. a prethermalization plateau. This order is dominant when a pair of tubes are inequivalent with, say, different temperatures or different effective interaction parameters, \gammaγ. When both tubes are in the strongly interacting regime we additionally characterize this non-thermal prethermalization plateau by constructing the quasi-conserved quantities that control the existence of this plateau as well as the associated generalized Gibbs ensemble.

Micrometric thermal electronic nose able to detect and quantify individual gases in a mixture

Recent urbanization and environmental problems urge for networks of sensors that can monitor air quality. Small, inexpensive, and smart sensors are one of the key components enabling the realization of such networks. Chemoresistive sensors are the ideal candidate, but they greatly lack selectivity, and for this reason, they are usually combined in arrays to create electronic noses, whose dimensions, however, make them not miniaturizable and cannot be integrated into portable devices. To overcome this inconvenience, we present a thermal electronic nose consisting of identical resistive sensors working at different temperatures so that the whole device is simple to make and tiny. The device contains two sensor arrays based on tin oxide nanowires decorated with Ag and Pt nanoparticles, respectively. The five sensors in each array are identical, but their response is differentiated by different temperatures locally generated by an on-chip integrated heater. This innovative approach allows the tiny array of five sensors together with the integrated heater to occupy only approximately 50 × 200 μm2 and consume only 120 μW. The tiny and portable device can estimate the concentration of H2 and NH3 in a mixture with a root mean square error of 6.1 ppm and 13.3 ppm, respectively, and it still works well after two months. The performance analysis of the double partial least squares regression used for concentration estimation also allows for feedback on which sensors are the most sensitive to which gas so that the electronic nose can be engineered for specific applications using the most suitable sensors. The size of the thermal electronic nose allows it to be integrated into portable and wearable devices, and its performance makes it suitable for any gas detection application. For example, a smartphone with an integrated sensor could carry out breath analysis and act as medical pre-screening or be used to evaluate the freshness of agri-food products in a rapid and non-invasive way.

An olfactory figure-ground segregation: The resistance fluctuation analysis of acetone gas for acetone/random gas mixtures recognition

Recognizing the disease-specific odor in human breath is a feasible non-invasive disease detection strategy. The most critical issue of olfactory diagnosis lies in recognizing the target gas among complex respiration compounds accurately. This paper demonstrated a novel approach to binary gas mixture recognition, exploring nature-related features of the target “odor object” (acetone gas) regardless of its “noisy background” (ethanol or methanol gases). Those features were extracted from time-series resistance fluctuations collected with one conventional SnO2 thin film gas sensor (TF) or our sensitivity-controllable lines gas sensor (TL). 6 features (variance, root mean square, band power, relative band power, entropy, and the number of values crossing mean value) were selected and analyzed with k-Nearest Neighbor regressor (KNN) and Support Vector Regressor (SVR). 18 types of acetone/ethanol mixtures and 3 acetone/methanol mixtures were tested with the proposed models and further explained with SHAP (SHapley Additive exPlanations). The model of TL and KNN achieved the optimal performance of regressing acetone concentrations with the coefficient of determination (R2) of 0.97 and 0.95 in individual gas and binary gas mixtures, respectively. Entropy was found to be the key feature that was linked to sensor properties. Finally, acetone gas data mixed with simulated gas and random noise data were experimented to evaluate our models’ potential for acetone/random gas mixture recognition and robustness.

Radiative Forcing From Halogen Reservoir and Halocarbon Breakdown Products

AbstractThe direct radiative forcing (RF) from halocarbons is reasonably well characterized. However, the forcing due to polyatomic halogen reservoir and halocarbon breakdown products has not previously been quantified and it is important to estimate this contribution. Four gases, ClONO2, COCl2, COF2 and COClF, are considered; their stratospheric abundances mostly originate from the breakdown of chlorofluorocarbons, hydrochlorofluorocarbons and CCl4. They have significant mid‐infrared absorption bands and peak stratospheric mole fractions ranging from around 20 ppt to over 1 ppb, which are large compared to typical abundances of many emitted halocarbons. Using satellite observations of stratospheric abundance, observed infrared spectra, and a narrow‐band radiation code, the stratosphere‐adjusted radiative forcings (SARF) is computed. The global‐annual mean SARF is estimated to be 7 ± 0.8 mW m−2 based on measured abundances in the period 2004–2019, with ClONO2 contributing about 50%. Whilst not a major contributor to anthropogenic RF, only six individual halocarbon gases cause a significantly greater forcing. This forcing is then approximately attributed to their source gases; for most, it modestly enhances (by 1%–3%) both their direct RF and their global warming potentials. The most significant enhancement (5%–15%) is to CCl4, the principal source of stratospheric COCl2 and contributor to ClONO2 abundances; disagreement in recent satellite‐based COCl2 retrievals is a significant source of uncertainty. These additional gases enhance the available best estimate of the total forcing due to halocarbon source gases (including e.g., ozone depletion) by about 3%; notably, this is the only identified indirect mechanism that increases, rather than decreases, total halocarbon forcing.

Impact of p-cresol on hydrogen sulfide and ammonia treatment by biotrickling filter and the production of nitrous oxide

Biotrickling filter (BTF) is often used for purification of waste gas from swine houses, with vital information still needed regarding interaction effects among multiple gas pollutants removal and also the formation of byproducts especially nitrous oxide (N2O, a strong greenhouse gas) due to the relative high NH3 concentration level compared to other gases. In this study, gas removal and N2O production were compared between two BTFs, where the inlet gas of BTF-1 contained NH3 and H2S while p-cresol was additionally supplied to BTF-2. At inlet load (IL) between 3.67 and 18.91 g m−3 h−1, removal efficiencies of NH3 exceeded 95% for both BTFs. As alternative strategy, adding thiosulfate improved H2S removal. Interestingly, presence of p-cresol to some extent promoted H2S removal at IL of 0.56 g m−3 h−1possibly due to effect on pH value of circulating solution. Similar to NH3, removal efficiencies of p-cresol were higher than 95% at an average IL of 2.98 g m−3 h−1. Gas residence time, pH of circulating solution and inlet loading were identified as key factors affecting BTF performance, but the response of individual gas compound to these factors was not consistent. Overall, p-cresol enhanced N2O generation although the effects were not always significant. High-throughput sequencing results showed that Proteobacteria accounted for the largest proportion of relative abundance and BTF-2 had much richer microbial diversity compared to BTF-1. Thermomonas, Comamonas, Rhodanobacter and other bacterial genus capable of denitrification were detected in both BTFs, and their corresponding abundances in BTF-2 (10.9%, 8.7% and 5.2%) were all greater than those in BTF-1 (0.4%, 0.3% and 2.0%), indicating that more denitrification may occur within BTF-2 and higher N2O could have been generated. This study provided evidence that organic gas components, served as carbon source, may increase the N2O production from BTF when treating waste gases containing NH3.

ИССЛЕДОВАНИЕ СОСТАВА БИОГАЗА НА ПОЛИГОНЕ ТКО «СТРЕЛЕЦКОЕ»

The purpose of the work is to summarize data on the analysis of the composition of biogas produced at the Streletskoye MSW landfill (Belgorod region) from 2017 to the present in order to assess its average composition and its changes. The composition and yield of biogas was studied in an experimental line of three wells and in four gas collection systems with a total area of 1.75 hectares, built at the site in 2017–2020. According to the results of aeration, the methane content without taking into account leaks ranges from 35 to 70% and is constant for each gas collection field. The practice of operating gas collection systems shows the great importance of sealing the body of landfills to prevent atmospheric air suction. Monitoring of the natural release of landfill gas from the landfill body in 471 wells showed an uneven nature of gas formation at different points, so the installation of individual observation and working gas wells may not allow determining the average indicators for the landfill. Studies of the composition and yield of biogas over a long period of time have shown the stability of the indicators and their dependence only on the technical characteristics of the landfill site. Seasonality has little effect on methane output, so surveys for climatic conditions similar to the Belgorod region can be carried out at any time of the year. The conducted studies showed that GOST 59417-2021 does not allow for an effective determination of biogas potential. To solve this problem, an express survey method is proposed with the installation of temporary wells and determination of biogas parameters using portable equipment.

Mobility Infrastructures and Health: Scoping Review of studies in Europe.

Movement-friendly environments with infrastructure favouring active mobility are important for promoting physical activity. This scoping literature review aims at identifying the current evidence for links between mobility infrastructures and (a) behaviour regarding active mobility, (b) health outcomes and (c) co-benefits. This review was conducted in accordance with the PRISMA scoping review guidelines using PubMed and EMBASE databases. Studies included in this review were conducted in Europe, and published between 2000 and March 2023. 146 scientific articles and grey literature reports were identified. Connectivity of sidewalks, walkability, and accessibility of shops, services and work are associated with walking. Cycling is positively associated with cycle-paths, separation of cycling from traffic and proximity to greenspaces, and negatively associated with traffic danger. Increased active transportation has a protective effect on cardiovascular and respiratory health, obesity, fitness, and quality of life. Co-benefits result from the reduction of individual motorized transportation including reduced environmental pollution and projected healthcare expenditure. Mobility infrastructure combined with social and educational incentives are effective in promoting active travel and reducing future healthcare expenses. A shift to active transportation would increase both individual and community health and decrease greenhouse gas emissions.

Towards a spatially resolved, single-ended TDLAS system for characterizing the distribution of gaseous species.

Many applications require diagnostics that can quantify the distribution of chemical gas species and gas temperature along a single line-of-sight, which is challenging in process environments with limited optical access. To this end, we present an approach that combines time-of-flight Light Detection and Ranging(LiDAR) with Tunable Diode Laser Absorption Spectroscopy (TDLAS)to scan individual gas molecular transition lines. This method is applicable in situations where scattering objects are distributed along the beam path, such as solid fuel combustion, or when dealing with multiple gas volumes separated by weakly reflecting windows. The approach is demonstrated through simulation studies and an initial experimental proof of concept for separated gas volumes.

Development of 2D CuO based chemiresistive sensors for detecting binary mixture of volatile organic compounds and investigation of the adsorption kinetics via Eley-Rideal mechanism

Detection of mixture of volatile organic compounds (VOCs) and prediction of the individual components present in it plays a significant role in various applications arching from health care to environmental monitoring. In this research, CuO nanoflakes of thickness ∼ 35 nm, and direct and indirect bandgaps of 1.8 and 1.3 eV, respectively, were synthesized hydrothermally. Chemiresistive sensors fabricated using the CuO nanoflakes were tested for individual gases and all combinations of binary mixtures of five VOCs namely acetone, acetonitrile, isopropanol, methanol, and toluene at 300 °C which was found to be the optimum temperature of operation for all the five VOCs. The response of CuO was found to range from 58–140 %, 35–95 %, 23–98 %, 17–50 %, and 5–30 % for 25–75 ppm of toluene, methanol, isopropanol, acetonitrile, and acetone respectively. The response was found to be an addition of the responses of CuO to the two individual VOCs when the sensing layer was exposed to the mixture of two at 300 ℃. The adsorption kinetics of the metal oxide for mixture of gases is explained using the Eley-Rideal mechanism. Also, the random forest (RF) algorithm was employed to identify the individual VOCs and their respective concentrations just by taking inputs from the response transients of CuO nanoflakes to the binary mixtures of VOCs and was found to exhibit 93.8 % accuracy.