Industrial Applications Research Articles

Evaluation of the mechanical characteristics of epoxy resin hybrid composites reinforced with date palm stem fibre and glass fibre

This study investigates the mechanical properties of epoxy resin hybrid composites reinforced with date palm stem fibres (DPSF) and glass fibres (GF). Natural fibres like DPSF offer numerous advantages, including low cost, renewability and biodegradability, making them environmentally friendly alternatives to synthetic fibres. GF, on the other hand, is well-known for its high strength and lightweight properties, making it ideal for various industrial applications. In this research, DPSF and GF were combined to create hybrid composites, aiming to enhance mechanical properties and reduce production costs compared to pure GF composites. The study involved the preparation of DPSF and the fabrication of composites using a hand lay-up approach with varying fibre proportions, followed by mechanical testing that included impact, flexural, tensile test and as well as a water absorption test. The results show that both the flexural and tensile strengths of a composite with epoxy resin containing 10 wt%DPSF and 10 wt%GF improved significantly, resulting in a tensile strength of 17.75 MPa and a flexural strength of 48.13 MPa, along with a favourable reduction in water absorption. The impact strength was significantly increased by the optimal integration of a composite with epoxy resin containing 20 wt% DPSF and 10 wt% GF. These findings suggest that the hybrid composites demonstrate enhanced mechanical properties, revealing the combined benefits of DPSF and GF. This study underscores the potential of DPSF-GF reinforced composites as a cost-effective and mechanically strong alternative to conventional GF composites.

Design and Computational Modelling of AUV Tunnel Thruster Covers for Efficient Operation

Autonomous underwater vehicles have seen widespread adoption across industrial, scientific, and defence applications. They are typically utilized to perform oceanic mapping, surveillance, and inspection-type missions. Hovering AUVs, used for inspection applications, are over-actuated vehicles incorporating multiple thrusters to enable multiple degrees of freedom control at a low velocity. These vehicles, however, are extremely energy-limited, owing to their restrictive structural design that prohibits large batteries. This necessitates careful hydrodynamic design to best utilize this limited energy storage. Of particular importance are the hydrodynamic propulsion efficiencies of these vehicles. Whilst the external structure of AUV platforms is relatively well-defined and hydrodynamically optimized, one area has seen limited focus and optimization. This is the immediate surroundings of the propulsion geometry and housing. In this body of work, we propose an adaptation to the traditional through-body tunnel thruster geometry of an over-actuated AUV platform. The modification is the inclusion of a retractable internal thruster cover. Subsequently, a comparison is provided between a clean-hull AUV configuration, one with open through-body thrusters, and one fitted with the designed cover geometry. A comprehensive computational fluid dynamics analysis is then converged and assessed using the Reynolds-Averaged Navier–Stokes equations. The drag and local flow fields are determined, where the covers are found to reduce the drag coefficient and total drag of the AUV by 9.51%, primarily due to a reduction of 9.91% in the pressure drag. These findings highlight the increased operational efficiency of the cover geometry and support the adoption of such covers for energy-constrained AUVs.

A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis

Efficient bearing fault diagnosis not only extends the operational lifespan of rolling bearings but also reduces unnecessary maintenance and resource waste. However, current deep learning-based methods face significant challenges, particularly due to the scarcity of fault data, which impedes the models’ ability to effectively learn parameters. Additionally, many existing methods rely on single-scale features, hindering the capture of global contextual information and diminishing diagnostic accuracy. To address these challenges, this paper proposes a Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation (MSCNN-SKD) for bearing fault diagnosis. The MSCNN-SKD employs a five-stage architecture. Stage 1 uses wide-kernel convolution for initial feature extraction, while Stages 2 through 5 integrate a parallel multi-scale convolutional structure to capture both global contextual information and long-range dependencies. In the final two stages, a self-distillation process enhances learning by allowing deep-layer features to guide shallow-layer learning, improving performance, especially in data-limited scenarios. Extensive experiments on multiple datasets validate the model’s high diagnostic accuracy, computational efficiency, and robustness, demonstrating its suitability for real-time industrial applications in resource-limited environments.

Preparation of hemp‐based biocomposites and their potential industrial application

AbstractBiocomposites are an emerging field in plastic industry. The incorporation of agri‐food waste in conventional or biobased polymer matrices allows the industry to lessen its carbon footprint, while producing recyclable plastic products. In this work, hemp hurd, a byproduct of hemp fiber production process, was used as a filler to prepare novel compostable composites in two different loadings (22% and 32% w/w respectively). Waste hemp hurds were selected due to their abundance and the lack of derived high‐value products. Three different polymers were used, namely poly[(tetramethyleneadipate)‐co‐(tetramethyleneterephthalate)] (PBAT) and poly(tetramethylene succinate) (PBS) as compostable polymers and a polyethylene elastomer for comparison. The bare polymers and prepared blends were then characterized for their structure and morphology by XRD and FESEM, as well as for their wettability, thermal and mechanical properties (namely energy absorbed on impact and flexural modulus). Structural and morphological characterization unveiled the different interactions occurring between hemp and the polymer matrices, suggesting the major impact of the type of used polymer when compared with biomass loading percentages. Mechanical tests showed that both PBAT and PBS blends properties are comparable to those of the pure polymers (i.e., in the case of PBAT the flexural modulus of the pure polymer was 82.2 MPa while the hemp‐loaded blends stood at around 50 MPa, whereas all PBS polymers and blends were 220 MPa, with no statistical difference between samples). These biomaterials have also similar thermal properties with respect to the bare polymers, underlining the promising potential as sustainable alternative to pure polymers.Highlights Waste hemp hurds were used to load biodegradable polymers at different percentages. Hemp hurd‐based biomaterials with excellent properties were prepared. Polymer flexural behavior was maintained even after hemp addition. Polymer‐hemp interactions for each polymeric matrix were elucidated. Hemp substitutes up to 32% w/w of polymer without affecting its properties.

A Comprehensive Review of CO2 Mineral Sequestration Methods Using Coal Fly Ash for Carbon Capture, Utilisation, and Storage (CCUS) Technology

CO2 emissions from fossil fuel combustion are the main source of anthropogenic greenhouse gases (GHGs). A method of reducing CO2 emissions is CCUS (carbon capture, utilisation, and storage) technology. One part of CCUS technology involves mineral sequestration as its final stage, utilisation, which can be carried out using natural raw materials or waste. This is a particularly interesting option for power and CHP plants that use coal as their primary fuel. Combustion processes produce fly ash as a waste by-product, which has a high potential for CO2 sequestration. Calcium fly ash from lignite combustion and fly ash from fluidised bed boilers have particularly high potential due to their high CaO content. Fly ash can be used in the mineral sequestration of CO2 via direct and indirect carbonation. Both methods use CO2 and flue gases. Studies conducted so far have analysed the influence of factors such as temperature, pressure, and the liquid-to-solid (L/S) ratio on the carbonation process, which have shown different effects depending on the ash used and the form of the process. Due to the large differences found in the properties of fly ash, related primarily to the type of fuel and boiler used, the process of mineral CO2 sequestration requires much research into its feasibility on an industrial scale. However, the method is promising for industrial applications due to the possibility of reducing CO2 emissions and, at the same time, recovering waste.

Chemical and Solvent-Based Recycling of DGEBA-Based Epoxy Thermoset and Carbon-Fiber Reinforced Epoxy Composite Utilizing Imine-Containing Secondary Amine Hardener.

Epoxy systems are essential in numerous industrial applications due to their exceptional mechanical properties, thermal stability, and chemical resistance. Yet, recycling epoxy networks and reinforcing materials in epoxy composites remains challenging, raising environmental concerns. The critical challenge is the recovery of well-defined molecules upon depolymerization. To address these issues, an innovative strategy is developed utilizing imine-containing secondary amine hardener (M1). The reaction of M1 with DGEBA produced high-performance epoxy thermoset P1, which exhibits Young's modulus of 2.18 GPa and tensile strength of 63.4 MPa, and excellent stability in neutral aqueous conditions. Upon carbon-fiber reinforcement, Young's modulus and tensile strength are significantly elevated to 10.99GPa and 328.3MPa, respectively. The reactive secondary amine functionalities enabled the tailored network to display a well-defined growth pattern, yielding only well-defined molecules and intact carbon fibers upon acidic depolymerization. Consequently, the recycled polymers retained properties identical to those of P1. Notably, it is discovered that despite the cross-linked nature of the epoxy networks, complete dissolution in dichloromethane facilitated straightforward solvent-based recycling, allowing the recovery of undamaged carbon fibers and an epoxy thermoset with properties matching the virgin material. Presented novel monomer design and approach showcased two important and efficient recycling options for epoxy systems.

Controlled Interfacial Tailoring of Hierarchical Silicon Synergizes Charge Transport Enabling Stable and Fast Lithium Storage.

Silicon is a promising anode material candidate but encounters volume change and capacity decay issues. Although diverse demonstrations in structural and interfacial engineering, the performance toward industrial applications remains to be improved. Herein, a controlled interfacial tailoring strategy is proposed for micro-nano hierarchically structured silicon. The resultant granules, consisting of randomly interconnected silicon debris modified by an electrically conductive carbon layer and a superionic sulfide conductor specifically in a controlled form (nanoparticles, coats, and matrices), attain distinctly different cyclic performances. As the carbon coating generally provides electron transfer paths for silicon, the introduced fast ion conductor exhibits a strong correlation with its configuration in facilitating ion transportation as well as improving the materials utilization and cyclic stability. Impressively, the granules encapsulated with a fast ion conductor layer show remarkably improved cycling performance and rate capability, attributable to a decent synergy of transmitting both electrons and lithium ions throughout the granule.

Insights into local wall erosion characteristics and prevention measures for cyclone separators

Cyclone separators are separation devices that use the principle of inertia to remove particulate matter from flue gases. The present study mainly focuses on wall erosion in cyclone separators and associated research. The main locations of erosion in gas–solid cyclone separators, including the entrance impact section, cyclone roof corner, vortex finder outer surface, spiral-type erosion strip, and lower cone section, are examined in detail. The main factors influencing wall erosion are discussed, including inlet flow velocity, solid particle properties and loading, geometrical structure, and manufacturing quality. Finally, several practically preventive measures against wall erosion are presented, including adjustment of operating conditions, the use of erosion-resistant materials, optimization of geometrical structures, and the addition of auxiliary devices, all of which are essential for ensuring operational efficiency, equipment reliability, safety, and environmental protection in various industrial applications. This paper aims to provide a basis for further research into erosion in cyclone separators as well as guidance for engineers involved in their industrial applications.

Optimisation of Flexible Forming Processes Using Multilayer Perceptron Artificial Neural Networks and Genetic Algorithms: A Generalised Approach for Advanced High-Strength Steels

Flexibility is crucial in forming processes as it allows the production of different product shapes without changing equipment or tooling. Single-point incremental forming (SPIF) provides this flexibility, but often results in excessive sheet metal thinning. To solve this problem, a pre-forming phase can be introduced to ensure a more uniform thickness distribution. This study represents advances in this field by developing a generalised approach that uses a multilayer perceptron artificial neural network (MLP ANN) to predict thinning results from the input parameters and employs a genetic algorithm (GA) to optimise these parameters. This study specifically addresses advanced high-strength steels (AHSSs) and provides insights into their formability and the optimisation of the forming process. The results demonstrate the effectiveness of the proposed method in minimising sheet metal thinning and represent a significant advance in flexible forming technologies applicable to a wide range of materials and industrial applications.

Performance of vibration and current signals in the fault diagnosis of induction motors using deep learning and machine learning techniques

Induction motors (IMs) play a pivotal role in various industrial applications, powering critical systems such as pumps, compressors, fans, blowers, and refrigeration and air conditioning systems. Monitoring the health of these IMs is essential for ensuring reliable operation. Numerous sensors, including vibration, current, temperature, acoustic, and power sensors, can be employed for their health monitoring. This article conducts a comprehensive comparative analysis of two widely used sensors—vibration and current, for classifying different health states of IMs, such as a healthy condition, bearing fault, and misalignment. The study employed deep learning techniques, specifically 1D and 2D convolutional neural networks, trained on raw data. Additionally, machine learning techniques, including random forest and XGBoost, were utilized and trained on features derived from preprocessed signals using fast Fourier transform and discrete wavelet decomposition. Comparative results indicated that vibration signals achieved remarkably high accuracy, nearly 100%, in detecting the investigated mechanical faults, while current signals, after signal processing and manual feature extraction, achieved an accuracy of 87.41%. These results demonstrate that, though current sensors are a viable alternative to vibration sensors, their performance can be affected by the type and degree of the considered faults. This study also highlights the attributes of vibration and current signals in the health monitoring of rotating machinery such as IMs.

A Programmable Gate Driver Module-Based Multistage Voltage Regulation SiC MOSFET Switching Strategy

Silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFETs), as a new material, have the advantages of low drain-source resistance, high thermal conductivity, low leakage current, and high switching frequency compared with silicon (Si)-based MOSFETs. Therefore, in many industrial applications, Si MOSFETs have been replaced by SiC MOSFETs. However, as the switching speed increases exponentially, some problems are amplified, the most serious of which is the overshoot of current and voltage. The increase in voltage and current slope caused by high switching speeds inevitably leads to overshoot, oscillations, and additional losses in the circuit. This paper focusses on the actual performance of the optimised switching strategy (OSS) in circuit testing and combines the existing simulation results to verify the practicability of OSS. In this paper, the optimised switching strategy is introduced first, and then, the LTspice model of SiC MOSFET is established in detail and verifies the feasibility of the OSS through half-bridge circuit simulation. Finally, the test platform is built using a programmable gate drive module (2ASC-12A1HP). Through a 400 V/30 A double-pulse test, the practicality of the OSS is verified. The experiments show that the OSS can greatly improve the switching performance of SiC MOSFETs.

Assessing the Drying Sensitivity of Alkali-Activated Binders Through Mechanical Reliability: Effect of Particle Size and Packing

Despite the steady progress of research on the alkali activation of wastes or subproducts from established industrial processes, the brittleness of the hardened alkali-activated materials frequently results in questionable mechanical reliability, particularly in industrial applications beyond construction materials. This work used a 33 factorial Design of Experiments to examine the effect of three different particle size distributions on the compressive strength and mechanical reliability (Weibull modulus) of a sodium silicate-activated blast-furnace slag under the same processing conditions. As expected, curing temperature and time were strongly correlated, and the corresponding response surfaces showed that, for all studied particle sizes, compressive strengths above 60 MPa with mechanical reliability above 5.0 could be obtained by curing at ~60 °C for ~40 h. The particle size differences caused no significant changes in the extent of alkali activation, as seen in the infrared-spectroscopy results. However, the intersection of the response surfaces showed that a coarser and narrower particle size distribution extended the working area (time × temperature) and favored mechanical reliability. Thus, the precursor’s particle size distribution, which governs particle packing and viscosity during processing, also determines the permeability of the set binder, which affects water removal during drying and the dried binder’s mechanical performance.

Unexpected inhibition roles of methane nanobubbles on hydrate decomposition

Uncontrolled hydrate decomposition poses extraordinary risks, such as submarine landslides during hydrate exploitation, and gas leakage during hydrate storage/transportation. Previous studies suggested inhibitors can effectively prevent hydrate decomposition by adsorbing onto the hydrate surface. However, herein, we find inhibitors have very limited adsorption coverages on hydrate, which cannot fully explain their good performance. In this study, by combining experiments and molecular simulations, a new inhibition mechanism is proposed, in which nanobubbles act as the primary factor in inhibiting hydrate decomposition. Specifically, when PVP (a typical inhibitor) is added into the solution, its hydrophobic groups interact strongly with CH4, thereby increasing CH4 solubility and inducing the formation of CH4 nanobubbles. These nanobubbles are then encapsulated within the hydrate and inhibit hydrate decomposition by the following process: nanobubbles gradually become exposed to the aqueous phase during hydrate decomposition and release CH4 to the region near the nanobubble-hydrate interface. The high local CH4 concentrations near the nanobubble-hydrate interface would promote the formation of microstructures necessary for hydrate nucleation, thereby amplifying the driving force toward nucleation and consequently inhibiting hydrate decomposition. Our proposed “nanobubble inhibition mechanism” can provide a theoretical foundation for developing and applying inhibitors to prevent hydrate decomposition in various industrial applications.

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

Advancements in Target Detection: A Comparative Analysis of Deep Learning Models for Real-Time Applications

Abstract. Target detection is a vital field within computer vision, playing an essential role in applications. This paper investigates the advancements and efficiencies of contemporary target detection methodologies, focusing on deep learning frameworks. Through a systematic review and evaluation of these models' architectures and performances using the Common Objects in Context (COCO) dataset, the study highlights their operational effectiveness and practical implications in real-world scenarios. A detailed comparative analysis is conducted, assessing the models based on mean Average Precision (mAP) and input dimensions to determine their suitability across various detection tasks. You Only Look Once version 3 (YOLOv3), in particular, is recognized for its ability to combine high-speed detection with significant accuracy, effectively addressing real-time processing challenges. The results confirm that YOLOv3, despite its smaller input size, performs comparably to more complex systems, demonstrating notable enhancements in design efficiency and processing speed. This research underscores the potential for future optimizations in model architectures to bridge the gap between high-speed and high-accuracy detection tasks, potentially transforming real-time applications across multiple sectors. The practical significance of this work lies in its ability to guide future developments in object detection, benefiting both academic research and industrial applications.

Cu-Based Praseodymium-Modified γ-Al2O3 Oxygen Carrier for Chemical Looping Combustion Process Optimization

Chemical looping combustion (CLC) emerges as a cost-effective CO2 capture technology, demonstrating high competitiveness for both industrial and energy applications. This study explores the synthesis of a Cu-based, praseodymium (Pr)-modified gamma-alumina-supported (20CuPA) oxygen carrier (OC) through the wet impregnation method and investigates its performance in CLC. The characteristics of the synthesized OC were investigated using field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, temperature-programmed reduction analysis, and X-ray diffraction analysis. The CLC of methane gas was performed in a thermogravimetric analyzer (TA-Q50). The oxygen transport capacity (OTC) of the 20CuPA-based OC was investigated for 10 redox cycles. The impact of temperature and time as process variables in determining the OTC of OCs was studied. The TGA results indicated that the most important factor influencing the optimization of the OTC of OCs was time. In comparison to time, temperature had less of an impact on the OTC of 20CuPA-OC. The maximum OTC of 20CuPA-OC, which was 0.0546 mg of O2/mg of OC, was reached using optimized process variables, including a temperature of 800 °C and a time of 3 min.

Reverse-Bent Modular Coil Structure with Enhanced Output Stability in DWPT for Arbitrary Linear Transport Systems

Dynamic wireless power transfer (DWPT) systems with segmented transmitters suffer from output pulsations during the moving process. Although numerous coil structures have been developed to mitigate this fluctuation, the parameter design process is complicated and restricted by specific working conditions (e.g., air gaps). To solve these problems, a novel reverse-bent modular transmitter structure is proposed for DWPT in industrial automatic application scenarios such as linear transport systems. Considering the heterogeneous current density distribution in the adjacent region between two coils which causes a drop in magnetic field, the proposed coil structure attempts to eliminate the effects of the adjacent region by bending the terminal parts of each coil reversely to the ferrite layer for shielding. Compared to traditional planar couplers, this structure array can generate a uniform magnetic field over various air gaps. A 100 W laboratory prototype was built to verify the feasibility of the proposed system. The experimental results show that the proposed system achieved a constant output voltage, and the output pulsation was within ±2.3% in the dynamic powering process. The average efficiency was about 88.29%, with a 200 mm transfer distance. When the air gap varied from 20 mm to 30 mm, the system could still retain constant voltage output characteristics.

The Application of PID Control in DC Motor Control Systems

Abstract. The application of Proportional-Integral-Derivative (PID) control in direct current (DC) motor control systems is essential for achieving high levels of precision and reliability in both industrial and robotic applications. As DC motors are widely utilized in environments requiring accurate speed and torque control, this study seeks to address common challenges such as overshoot, instability, and sluggish response times that can compromise performance. The research is centered on the development and implementation of a PID-based control strategy using MATLAB/Simulink as the primary tool for simulation and testing. The study involves detailed simulation models and experimental validation, using data collected from controlled lab environments to evaluate the effectiveness of various tuning methods. These methods include adjusting the proportional, integral, and derivative gains to optimize motor responsiveness and system stability. The results indicate that with appropriate tuning, PID controllers can significantly enhance DC motor performance, leading to reduced overshoot, faster response times, and greater overall system reliability. The research concludes that adaptive PID control strategies, which dynamically adjust parameters in response to changing operating conditions, provide a robust solution for improving DC motor control in complex, dynamic, and nonlinear environments. This study lays the groundwork for future innovations in automation and precision-driven industries, offering insights that could be applied across a wide range of applications.

StarchCrete: A starch-based biocomposite concrete for lightweight building material applications

StarchCrete is a novel composite material that utilizes starch as a biobased binder, replacing traditional inorganic binders. The composite was prepared using varying hemp shives ratios (from 0−1) at constant sand: corn starch: water ratio (5:1:1). The mixture was manually compacted into a mold and heated under microwave radiation. The effect of different starches on a composite’s compressive strength was investigated. The result reveals that high-viscosity starch leads to strong compressive strength. Increasing the hemp shives ratio significantly reduced thermal conductivity (from 0.52 to 0.15 W/(m·K)) and thermal diffusivity (from 0.85 to 0.35 mm2/s). With its strong compressive strength (2.8 MPa), low density (887 kg/m3), and low thermal conductivity (0.18 W/(m·K)), the hemp shives ratio of 0.5 was deemed appropriate. Furthermore, coating the material with paraffin wax improved its water resistance, extending its durability from 7 to 16 days, making it suitable for industrial applications.

Towards maximizing biomass and lipid productivity: high-throughput screening assay for prospecting heterotrophic growth for new microalgal isolates

BackgroundMicroalgae have emerged as sustainable alternatives to fossil fuels and high-value petrochemicals. Despite the commercial potential of microalgae, their low biomass productivity is a significant limiting factor for large-scale production. In the photoautotrophic cultivation of microalgae, achievable cell density levels depend on the light transmittance of the production system, which can significantly decrease the photosynthetic rate and biomass production. In contrast, the mixotrophic cultivation of microalgae using heterotrophic carbon sources enables high-density cultivation, which significantly enhances biomass productivity. The identification of optimal production conditions is crucial for improving biomass productivity; however, it is typically time- and resource-consuming. To overcome this problem, high-throughput screening (HTS) system presents a practical approach to maximize biomass and lipid production and enhance the industrial applicability of microalgae.ResultsIn this study, we proposed a two-step HTS assay that allows effective screening of heterotrophic conditions compatible with new microalgal isolates. To confirm the effectiveness of the HTS assay, three microalgal isolates with distinctive morphological and genetic traits were selected. Suitable cultivation conditions, including various heterotrophic carbon sources, substrate concentrations, and temperatures, were investigated using a two-step HTS assay. The optimized conditions were validated at the flask scale, which confirmed a significant enhancement in the biomass and lipid productivity of each isolate. Moreover, the two-step HTS assay notably enhanced economic and temporal efficiency compared to conventional flask-based optimization.ConclusionsThese results suggest that our two-step HTS assay is an efficient strategy for investigating and optimizing microalgal culture conditions to maximize biomass and lipid productivity. This approach has the potential to enhance the industrial applicability of microalgae and facilitate the seamless transition from laboratory to field applications.