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Item Enhancing grassland nitrogen estimation : a multiscale approach through optical reflectance spectroscopy and hybrid modeling techniques : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Palmerston North, New Zealand(Massey University, 2025-01-21) Dehghan-Shoar, Mohammad HossainOptical remote sensing technology has emerged as a powerful tool for assessing vegetation characteristics, particularly nitrogen (N) concentration (N%) in heterogeneous grasslands. Accurate estimation of N% is crucial for farmers, as it directly influences grassland productivity and plays a key role in sustainable land management. Accurate N assessments optimize fertilizer use, boosting productivity, lowering costs, and enhancing environmental modeling to address impacts such as N leaching and greenhouse gas emissions. Despite significant progress, challenges and knowledge gaps remain, highlighting the need for continued research to fully harness remote sensing’s potential in agricultural management and its impact on livestock productivity. This thesis aims to advance the accurate estimation of grassland N% by integrating physically-based, empirical-statistical, and hybrid models using optical reflectance spectroscopy data. The research focuses on three primary objectives: 1. To estimate N% in grasslands using optical reflectance spectroscopy, data will be collected across multiple scales, including ground-, leaf-, canopy-, and satellite-scale observations. 2. To improve the universality and adaptability of grassland N% models through a hybrid approach that combines data from various optical sensors across multiple scales. 3. To account for and quantify uncertainties in grassland N% prediction models. The thesis addresses the challenge of uncertainty by conducting a comprehensive analysis of its sources and developing methods, such as Physically Informed Neural Networks (PINN), to account for them. Key strategies include data fusion techniques for integrating diverse data sources and improving atmospheric correction methods. A unified methodology combining empirical-statistical and physically-based approaches is proposed to enhance generalization. Machine learning algorithms play a pivotal role in feature selection and optimization, further improving model accuracy and transferability. The developed methods undergo evaluation using independent validation data collected from heterogeneous grasslands across different periods and locations. Results demonstrate that integrating physically-based and empirical-statistical approaches significantly improves model accuracy and transferability, providing a deeper understanding of the factors influencing vegetation traits. This thesis highlights the importance of advanced techniques, including machine learning, deep learning algorithms, Radiative Transfer Models (RTM), and data fusion methods, for precisely characterizing vegetation traits, contributing to more sustainable and efficient grassland management practices.Item Enhancing the Precision of the Self-Compassion Scale Short Form (SCS-SF) with Rasch Methodology(Springer Science+Business Media, LLC, part of Springer Nature, 2024-10-28) Adu P; Popoola T; Bartholomew E; Iqbal N; Roemer A; Jurcik T; Collings S; Aspin C; Medvedev ON; Simpson CRObjectives: Precise measurement of self-compassion is essential for informing well-being–related policies. Traditional assessment methods have led to inconsistencies in the factor structure of self-compassion scales. We used Rasch methodology to enhance measurement precision and assess the psychometric properties of the Self-Compassion Scale Short Form (SCS-SF), including its invariance across Ghana, Germany, India, and New Zealand. Method: We employed the Partial Credit Rasch model to analyse responses obtained from 1000 individuals randomly selected (i.e. 250 from each country) from a total convenience sample of 1822 recruited from the general populations of Germany, Ghana, India, and New Zealand. Results: The initial identification of local dependency among certain items led to a significant misfitting of the SCS-SF to the Rasch model (χ2 (108) = 260.26, p < 0.001). We addressed this issue by merging locally dependent items, using testlets. The solution with three testlets resulted in optimal fit of the SCS-SF to the Rasch model (χ2 (27) = 23.84, p = 0.64), showing evidence of unidimensionality, strong sample targeting (M = 0.20; SD = 0.72), and good reliability (Person Separation Index = 0.71), including invariance across sociodemographic factors. We then developed ordinal-to-interval conversion tables based on the Rasch model’s person estimates. The SCS-SF showed positive correlations with measures of compassion towards others, optimism, and positive affect, alongside negative associations with psychological distress and negative affect. Conclusions: The current study supports the reliability, as well as the structural, convergent, and external validity of the SCS-SF. By employing the ordinal-to-interval conversion tables published here, the precision of the measure is significantly enhanced, offering a robust tool for investigating self-compassion across different cultures.Item Real-time measurement of fill volume in a vessel using optical and acoustical means : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū, New Zealand. EMBARGOED until further notice.(Massey University, 2024) Barzegar, Mohammad AminThis thesis investigates optical and acoustical methods for quickly determining the fill volume in cavities, vessels, or hoppers. The motivation for this study was the demand in the New Zealand aerial topdressing industry for a system that can accurately track the fill volume of vessels containing powders. A notable challenge in this industry is that topdressing aircraft lack systems for measuring the volume of discharge vessels during flight, leading to issues with flight safety and operational efficiency. This thesis specifically addresses the challenge of real-time fill volume determination in hoppers within New Zealand's aerial topdressing industry. Additionally, the outcomes of this thesis may offer insights and applicable methods for other industrial and scientific sectors that require real-time, contactless volume determination techniques. Three contactless volume measurement approaches were investigated: ultrasonic range-finding, 3D scanning, and acoustical resonance. The first approach used an array of ultrasonic rangefinders installed in a 200-litre powder-containing vessel, resulting in material level readings from multiple points. This technique was tested under discharge and no-flow conditions. According to the results, this method provided readings of the vessel fill volume with a measurement rate of ~1 Hz and an uncertainty of ~3% of the vessel capacity. The second approach used stereoscopic technique to provide real-time scans of the material surface in the vessel. A model was developed for calculating the volume of material in the vessel using the vessel internal scans. According to the test results on different bulk materials under discharge and no flow conditions for two vessels of sizes 50 and 200 litres, the real-time fill volume of the vessel was obtained with uncertainties less than 1% of the vessel’s volume. The third approach explored Helmholtz Resonance for determining the volume of powders and solids. This involved studying the impact of inserting a sample into Helmholtz Resonators on resonance parameters. Three models were developed for volume estimation: an extended Helmholtz Resonance model modifying the classical equation for resonators with long ports, a model for estimating solid volume in powders based on resonance frequency and quality factor, and a model for instantaneous volume measurement of a vessel's empty cavity using Helmholtz Resonance. The latter correlated the change in cavity sound pressure to its volume, showing it could accurately determine volume in real-time with less than 0.1% error relative to the vessel capacity.Item The relationship between lean and performance measurement in service and manufacturing organisations in New Zealand : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Accounting at Massey University, Manawatū, New Zealand(Massey University, 2024-08-20) Roos, Christina MariaThis thesis examines the relationship between lean and performance measurement systems (PMSs) in New Zealand private and public organisations. There is a dearth of research on lean and PMSs, despite the importance of understanding this relationship. To provide insights and an understanding of this relationship, this study identifies and examines lean techniques and the corresponding performance measures applied to measure lean performance. The research data were analysed using Searcy’s (2004) framework of lean performance dimensions and the findings were informed by contingency theory. The research conclusions were drawn from qualitative interpretations of the data through thematic analysis. The research findings show that lean is still in an emergent state in New Zealand and that managers associate lean with reducing waste to reduce costs, promote continuous improvement, improve quality, and deliver customer value. This differs from the global perspective of providing customer value through continuous improvement (Thornton et al., 2019; Albzeirat et al., 2018). The lean techniques implemented by the lean organisations reflect the managers’ association of lean with reducing cost and promoting continuous improvement, with a marginal focus on improving customer value. Organisations that successfully use lean techniques remain in a ‘black hole’ between measuring lean performance and the inclusion of lean performance dimensions in the PMS. Less than half of the organisations adapted their PMSs to include lean performance, nor did they implement specific lean KPIs to measure and evaluate lean performance. In those organisations where managers had identified, implemented, and used critical lean success factors, they had concurrently modified their PMS to include lean KPIs. Nonetheless, dollars saved are still recognised as the most important lean contribution, subsequently, once dollar-related goals were reached, organisations restored their traditional PMSs. As such, lean performance was neglected, and the existing lean practices were not associated with PMS. Ultimately, most organisations did not adapt their PMSs sufficiently to accommodate lean, and consequently, the organisations’ PMSs did not adequately capture lean outcomes. The implications for organisations and CEOs are that they need to shift focus from cost savings and profits to lean techniques and map the correct key performance indicators to the PMS to fully measure and evaluate lean outcomes.Item Cross-cultural measurement validation: an analysis of dissent, workplace freedom of speech, and perceived immediacy(Taylor and Francis Group on behalf of the Eastern Communication Association, 2024-03-12) Croucher SM; Kelly S; Ashwell D; Condon S; Tootell BCroucher and Kelly (2019) laid out guidelines to develop measures that can be used across cultures. The present study provides support for their guidelines, indicating that pancultural measurements cannot be behavioral and should not include unnecessary contexts; however, they should be worded as simplistically as possible. This study utilizes measures of dissent, perceived immediacy, and workplace freedom of speech in Australia, Canada, New Zealand, and the United States. Only the perceived immediacy measure, which follows Croucher and Kelly’s (2019) guidelines, maintained internal consistency.Item Machine learning based calibration techniques for low-cost air quality sensors : thesis for Doctor of Philosophy, Electronic and Computer Engineering, Massey University(Massey University, 2024-05-28) Ali, Mohammad SharafatBreathable air is the single most essential element for life on earth. Polluted air poses numerous risks to health and the environment, especially in urban areas with large populations and many active sources of air pollution. Therefore, researchers from a wide range of disciplines have been working on mitigating the impact of air pollution. Monitoring ambient air pollution is one of the means to ensure public health safety, raise public awareness and build a sustainable urban environment. However, conventional air quality monitoring stations are mostly confined to a few locations due to their costly equipment and large sizes. As a result, although these monitoring stations provide accurate air pollution data, they can only offer a low-fidelity picture of air quality in a large city, leading to a poor spatial resolution of urban pollution data. Low-cost sensor (LCS) technologies aim to address this challenge and intend to make it possible to monitor air quality at a high spatio-temporal resolution. The pollutant data captured by these LCSs are less accurate than their conventional counterparts and thus require calibration techniques to improve their accuracy and reliability. Researchers have proposed different calibration methods and techniques to improve the accuracy of the LCSs, including machine learning based calibration models. This thesis investigates and proposes several machine learning-based calibration techniques and rigorously benchmarks their performance using a robust training, validation and testing method. Based on the findings, One Dimensional Convolutional Neural Network (1DCNN) and Gradient Boosting Regression (GBR) based calibration techniques provide consistently accurate performance. Both of these machine learning techniques, which have not been widely used or evaluated for low-cost ambient gas sensor calibration, can improve the state of the art. This research also demonstrates that readily available and previously unemployed co-variate data, namely the number of days the sensor has been deployed and the time of day at which the reading is taken, can significantly improve the accuracy of Machine Learning based calibration algorithms.Item Thin film electrochemical sensor for water quality monitoring : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand(Massey University, 2023-12-11) Lal, KartikayFreshwater is the most precious natural resource, essential for supporting life. Aquatic ecosystems flourish in freshwater sources, and many regions around the world depend on aquatic food sources, such as fish. Nitrogen and phosphorous are the two nutrients, in particular, that are essential for growth of aquatic plants and algae. However, with rising population and anthropogenic activities, excessive amounts of such nutrients enter our waterways through various natural processes, thereby degrading the quality of freshwater sources. Elevated levels of nitrate-nitrogen content, in particular, lead to consequences for both aquatic life as well as human health, which has been a cause for concern for many decades. As recommended by the World Health Organization, the maximum permissible nitrate level in water is 11.3 mg/L. These levels are often exceeded in coastal areas or freshwater bodies that are close to agricultural land. Therefore, it is essential to monitor nitrate levels in freshwater sources in real-time, which can be achieved by employing detection methods commonly used to detect ionic content in water. Hence, a comprehensive review was carried out on various field-deployable electrochemical and optical detection methods that could be employed for in-situ detection of nitrate ions in water. The primary focus was on electrochemical methods that could be integrated with low-cost planar electrodes to achieve targeted detection of nitrate ions in water. Designing resilient sensors for real-time monitoring of water quality is a challenging task due to the harsh environment to which they are subjected. There is a significant need for sensors with attributes such as repeatability, sensitivity, low-cost, and selectivity. These attributes were first explored by evaluating the performance of silver and copper materials on three distinct geometric patterns of electrodes. The experiments produced promising results with interdigitated pattern of copper electrodes that were successful in detecting 0.1-0.5 mg/L of nitrate ions in deionised water. The interdigitated geometric pattern of electrodes were further analyzed in four distinct materials namely, silver, gold, copper, and tin with real-world freshwater samples that were collected from three different freshwater bodies. The water samples were used to synthesize varying concentrations of nitrate ions. The results showed tin electrodes performed better over other materials for nitrate concentrations from 0.1-1 mg/L in complex matrix of real-world sample. The nitrate sensor eventually needs to be deployed in freshwater bodies, hence a real-time water quality monitoring system was also built that incorporated sensors to monitor five basic water quality parameters with the aim to monitor and study the quality of water around the local area.Item Amplifying the power of proximal sensing techniques to assess the cadmium concentration in agricultural soils : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand(Massey University, 2023) Shrestha, GautamCadmium (Cd) accumulation in agricultural soils due to long-term phosphate fertiliser applications has raised concerns in New Zealand and globally due to the potential toxicity of Cd in food products. Elevated soil Cd concentration can enhance Cd availability for plant uptake, increasing the risk of food chain transfer. Cadmium management is generally achieved through reference laboratory methods to estimate Cd concentration in soil and plant samples. Reference laboratory methods of Cd analysis are precise; however, sample preparation and associated resource cost make them expensive. As a complementary method, proximal sensing techniques including visible-near-infrared (vis-NIR: 350–2500 nm), mid-infrared (MIR: 4000–400 cm-1) reflectance and portable X-ray fluorescence (pXRF: 0–40 keV) spectroscopy have been successfully used to monitor elevated Cd levels in mining areas and in plants showing stress or toxicity symptoms due to Cd. However, application of such technologies in agricultural soils with low Cd concentration are relatively understudied. Hence, this study was conducted to amplify the power of three proximal sensing techniques to quantify Cd in soil samples from diverse soil orders, climatic conditions, land uses, and vegetations and plant samples for cost-effective Cd monitoring at regional to farm scale. In this doctoral study, soil and plant samples were scanned using vis-NIR, MIR, and pXRF sensors. Topsoil samples were obtained from (1) the Otago-Southland regional survey (n=622), (2) a pastoral farm survey (n=87) including dairy and sheep and beef farms with long-term phosphate fertiliser application history, and (3) two independent glasshouse experiments using Pallic and Allophanic soils amended with increasing soil Cd concentrations, and with or without a model forage herb, chicory (Cichorium intybus L.). In both experiments, chicory aboveground biomass and root samples were scanned using the three sensors, along with a periodic collection of vis-NIR spectra from soil and plant in-situ. Total Cd was determined in all samples, while the distribution of Cd among geochemical fractions was studied in the pastoral farm survey samples only. Reference laboratory results and spectral information were combined to develop models for accurate Cd predictions. For regional survey samples (n=622, 0.01–0.56 mg Cd/kg) including agricultural soils (47%), validation (v) results (n=124, 0.01–0.43 mg Cd/kg) showed Granger-Ramanathan model averaging of outputs from models using individual pXRF, vis-NIR, and MIR data as input for partial least squares (PLS) – support vector machine regression performed optimally to quantify total soil Cd with normalised root mean square error (nRMSEv) of 37% and concordance correlation coefficient (CCCv) of 0.84. For agricultural soils (n=84, 0.10–1.20 mg Cd/kg), cross-validation (cv) results of models using individual vis-NIR, MIR, and pXRF data as input for PLS performed with nRMSEcv of 26%, 30%, and 31% and CCCcv of 0.85, 0.77, and 0.75 respectively. For acid soluble (0.01–0.27 mg Cd/kg) and organic matter bound (0.02–0.27 mg Cd/kg) Cd, models using vis-NIR data performed with nRMSEcv of 11% and 33% and CCCcv of 0.97 and 0.84, respectively. For exchangeable (0.003–0.25 mg Cd/kg) Cd, a model using MIR data as input performed with nRMSEcv of 40% and CCCcv of 0.57. Using the Otago and Southland regional survey soil samples spectra as a soil spectral library (SSL), Cd concentration in the local set (agricultural soil samples) were quantified. A model using MIR data from the regional SSL pastoral soil subset (n=283, 0.01–1.31 mg Cd/kg) spiked with selected local set samples (n=12) with weights (×4) as input for LOCAL algorithm quantified local soil Cd with nRMSE of 38% and CCC of 0.78. In the glasshouse experiments, Cd translocation factor (TF) values for chicory were calculated using proximal sensor data and the results showed a significant relationship (R2=0.74, p<0.001) between measured and predicted TF values. A model using in-situ leaf clip vis-NIR spectra showed optimal performance to assess Cd concentration in aboveground chicory biomass with nRMSEcv of 28% and CCCcv of 0.93. Among vegetation indices calculated ‘blue green index 2’ showed a significant (p<0.01) R2 value (0.19, 0.36) in both experiments. Models using pXRF spectra as input showed optimal performance to predict chicory root (n=28, 0.86–25.79 mg Cd/kg) and Allophanic soil (n=112, 0.41–4.81 mg Cd/kg) Cd with nRMSEcv of 16% and 9% and CCCcv of 0.95 and 0.99, respectively. A model using laboratory vis-NIR spectra showed optimal performance to quantify Pallic soil Cd (n=336; 0.17–5.45 mg Cd/kg) with nRMSEcv of 22% and CCCcv of 0.97. Optimal prediction models using proximal sensor data can potentially be used for rapid cost-effective analysis of Cd concentration in soil and plant samples. Quantitative models for soil Cd using a combination of complementary proximal sensors data and chemometrics could feasibly be deployed for long-term monitoring of soil Cd at concentrations below pXRF detection limits and with reduced matrix interference from organic matter when compared to the individual techniques alone. The use of proximal sensing techniques to determine total soil Cd concentration in New Zealand agricultural soils has the potential to improve the scale and scope of long-term repeated monitoring of soil Cd concentration required under the framework of the national Tiered Fertiliser Management System. Reflectance spectroscopy could potentially be implemented to monitor plant-available and potentially-available soil Cd fractions to minimise plant Cd uptake. The use of a large soil spectral library to assess the local Cd concentration in agricultural soils could reduce the analytical cost to the farmers and allow intensive spatial and temporal monitoring of pastoral farms based on spectral analysis only. The use of in-situ and laboratory proximal sensor data to calculate bioconcentration and translocation factors could potentially support the evaluation of Cd food chain transfer risks. The spectral library developed from this doctoral study, including soil and plant root and aboveground biomass pXRF, vis-NIR, and MIR spectra with a wide range of Cd concentration can be used as reference materials for field level and airborne remote sensing studies.Item Exposure to fumigants and residual chemicals in workers handling cargo from shipping containers and export logs : a study of exposure determinants and neuropsychological symptoms : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Public Health (Epidemiology) at Massey University, Wellington, New Zealand(Massey University, 2022) Hinz, RuthFumigants are widely used in shipping containers and on export logs for biosecurity reasons. This thesis aimed to: (i) assess concentrations of fumigants and off-gassed chemicals in closed containers; (ii) identify container characteristics associated with high concentrations; (iii) assess personal exposure levels of workers exposed to these chemicals; and (iv) assess whether exposed workers report more neuropsychological symptoms. Air samples were collected from 490 sealed containers and at opening of 46 containers, and 193 personal full-shift air samples were collected for 133 container handlers, 15 retail workers, 40 workers loading export logs, and 5 fumigators. Samples were analysed by Selected Ion Flow Tube Mass Spectrometry for several common fumigants and harmful chemicals frequently found in shipping containers. Levels were compared to the New Zealand Work Exposure Standard (WES) and the Threshold Limit Value (TLV). A neuropsychological symptom questionnaire was completed by 274 container handlers, 38 retail workers, 35 fumigators, 18 log workers, and a reference group of 206 construction workers. Fumigants were detected in 11.4% of sealed containers (ethylene oxide 4.7%; methyl bromide 3.5%). Chemicals other than fumigants were detected more frequently, particularly formaldehyde (84.7%). Some cargo types (e.g. rubber products) and countries of origin (e.g. China) were associated with higher chemical concentrations. Fumigants were detected in both fumigated and non-fumigated containers. Ambient chemical concentrations in closed and just opened containers regularly exceeded the NZ WES and TLV. Personal exposure measurements never exceeded the NZ WES, although for 26.2% of samples the TLV for formaldehyde was exceeded. Duration spent unloading containers was associated with higher levels of ethylene oxide, C2-alkylbenzenes and acetaldehyde. Exposed workers were more likely to report ≥10 symptoms, and particularly for the fatigue domain. Longer cumulative duration of unloading containers was associated with more symptoms (Odds Ratio (OR) 7.5, 95% Confidence Interval (CI) 1.7-32.8), and specifically for symptoms in the memory/-concentration domain (OR 6.8, 95%CI 1.5-30.3), when comparing the highest exposure duration tertile to the lowest. In conclusion, while workers’ full-shift exposure levels to container chemicals are lower than previously expected (based on the high levels measured in closed containers), they may nonetheless cause long-term health effects.Item Printed sensors for indoor air quality : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Albany, New Zealand(Massey University, 2022) Rehmani, Muhammad Asif AliOn average, a human inhale about 14,000 litres of air every day. The quality of inhaled air is highly important as the presence of pathogens and contaminants in air can adversely affect human health. Generally, the probability of pathogens/contaminant is high in indoor environment where humans spend an estimated 90% of their total lifetime. Continuous urbanization, increasing population, technological advancement and automation has further increased the time spent indoors. The length of exposure and indoor activities such as cooking, smoking, ventilation and frequency of cleaning can further aggravate the health risk due to localized higher concentrations of the contaminants. According to the Environmental Protection Agency (EPA), poor indoor air quality (IAQ) is considered one of the top environmental dangers to the public as increasing number of people are suffering from asthma, allergies, heart disease, and even lung cancer. In New Zealand, poor air quality is estimated to cause 730 premature deaths and cost over one billion dollars in restricted activity days per year. The above premise cannot be validated until and unless there are means and measures of continually monitoring the indoor air pollutants with emphasis that the same can be fabricated using low cost and energy efficient methods. Furthermore, any remedial actions cannot be undertaken if the quantitative values of the environmental pollutants are unknown. Existing solutions for the air quality monitoring are expensive and can only be applied in certain numbers, leaving areas of the houses, offices, and schools unmonitored. Therefore, a ubiquitous system of air quality monitoring is needed, the one that can be applied on large areas like walls, roofs and so on. Such a prevalent system will allow sensing of air quality parameters rapidly, continuously, and with low power consumption. To realize the bigger objective of achieving sensing and aware surfaces for indoor air quality, this research proposes to print sensors on large surfaces rather than making them in batches and packaging in discrete units. Recent advancements in inkjet printing provide solutions which can enable the implementation of such sensors. However, the choice of inkjet printing method has major impact on the efficacy of printed sensors. Therefore, we have explored printing techniques based on conventional screen printing and non-conventional electrohydrodynamic (EHD) inkjet printing. These printing methods offer low-cost, rapid prototyping and high-thorough-put conductive printing of features as compared to other inkjet printing methods with the latter bringing further advantages of improved resolution, scalability, customization and little or no environmental waste printing solution. For screen printing, laser ablation process has been used to implement several customized transduction schemes. The utility of this technique is demonstrated by humidity sensing. It has been found that the designs of the transduction electrodes can easily be customized, and large area printing can be realized on the substrate. The fabricated humidity sensor provides higher sensitivity through bio-compatible sensing layer with good response and recovery time. Next, EHD printing was explored for high-resolution conductive printing on flexible substrates. Current EHD printing focuses on improving the print resolution by decreasing the printhead nozzle diameter thus limiting the type of ink to be used for printing purpose. In the proposed EHD printhead design we overcame this major shortcoming by improving the resolution of printed feature with a bigger nozzle of 0.5 mm diameter. This resulted in the printed feature resolution of less than 10 µm in general with the highest achieved resolution 1.85 µm. The effective nozzle diameter to printed feature ratio of more than 250 was achieved. The use of bigger nozzle for fine resolution printing opens the avenue for utilizing higher concentration of metallic nano-particles inks through EHD printing. The hallmark of the presented EHD printhead design is the utilization of off-the-shelf components which does not require expensive manufacturing process while highlighting the importance of wetting area profile of the nozzle to facilitate fine resolution printing which until now has not been explored in detail. Furthermore, the work highlights the issue of crack development during EHD printing in the conductive tracks while using available piezoelectric inkjet ink. Later the ink was modified to minimise the cracks in EHD printed features. Finally, a comprehensive study on the 3D printed microfluidic channels was conducted. The study highlights the variation of pressure developed in different microfluidic channel designs and the susceptibility of leakages from microfluidic devices. The work presents the possibility of utilizing the 3D printed microfluidics with printed sensors for deploying as lab-on-a-chip in various applications, such as passing a stream of air through sensors integrated in a microfluidic device for analysing the volatile organic compounds, humidity, toxic gases, and other analytes of interest. Overall, the presented work demonstrates the feasibility of using conventional and non-conventional printing methods through simple implementations for the fabrication of IAQ sensors with high degree of customization, low processing cost and scalability.
