Applied Science and Engineering Progress

Advances in Leaf and Canopy Temperature Sensors for Precision Irrigation: A Review

2025-10-24T11:22:36+07:00

Precision technologies are crucial for sustainable water management, as water scarcity and ineffective irrigation techniques continue to pose significant challenges in agriculture. One of the bases of plant-based irrigation scheduling is plant canopy temperature, which has become a reliable indicator of crop water status. The primary sensor technologies used to measure the temperature of leaves and canopies are discussed in this review, including integrated circuit sensors, thermistors, thermocouples, infrared thermometers, and infrared thermal imaging systems. Thermistors and thermocouples provide precise and affordable point-based measurements, but their scalability and installation are limited. For real-time canopy monitoring, infrared thermometers and thermal imaging provide non-contact options. Despite their higher price, thermal cameras enable the analysis of spatial variability. Low-cost irrigation system automation is made feasible by integrated circuit (IC) sensors, like the LM35, which combine accuracy and affordability. Research confirms that under deficit irrigation strategies, canopy temperature-based indices, notably the Crop Water Stress Index (CWSI), improve water use efficiency and enhance yield responses. However, sensor calibration, environmental variability, and the balance between accuracy and cost continue to be ongoing challenges.

Exploring the Potential of Cationic Modified Microfibrillated/Nanocellulose as Slow/Controlled Release Fertilizers: A Review

2025-10-27T09:41:52+07:00

Conventional controlled-release fertilizers often rely on synthetic polymer coatings that are costly, non-biodegradable, and environmentally harmful. Biodegradable alternatives such as microfibrillated cellulose and nanocellulose have attracted significant attention for use as sustainable fertilizer coatings. Their high surface area and mechanical strength enable improved nutrient retention, moisture resistance, and reduced leaching. Cationic modification further enhances coating adhesion and nutrient binding, offering superior control of nutrient release compared with unmodified cellulose. This review evaluates recent progress in the development of cationic cellulose-based coatings, including cost considerations, biodegradation behavior under soil conditions, physicochemical interactions that regulate nutrient release, and potential industrial applications. Challenges and future directions are also discussed, highlighting the role of cationic cellulose in advancing environmentally friendly fertilizer technologies.

Advances and Emerging Alternatives in Modified Cellulose Nanocrystals for Elastomer Reinforcement: A Review

2025-11-04T08:56:34+07:00

Cellulose nanocrystals (CNCs) are increasingly recognised as sustainable nanofillers for elastomer composites due to their biodegradability and excellent mechanical properties. However, their inherent hydrophilicity limits compatibility with hydrophobic elastomer matrices, creating significant challenges for effective reinforcement. To overcome this, significant progress has been made through surface modification strategies. Physical approaches such as adsorption and plasma treatment improve compatibility via non-covalent interactions. While chemical routes including etherification/esterification, grafting, silylation, and nucleophilic modification introduce covalent bonds that adapt CNCs surface chemistry to polymer matrices. Such modifications have been shown to reliably improve dispersion, strengthen interfacial adhesion, and enhance both the thermal stability and mechanical performance of elastomer composites when compared with those reinforced by unmodified CNCs. Lignin-containing CNCs (LCNCs) offer distinct advantages by combining inherent hydrophobicity, thermal shielding, and simpler processing, making them a promising bio-based alternative to extensively modified CNCs (M-CNCs). Nonetheless, industrial implementation remains constrained by process complexity, high costs, and performance trade-offs at different filler loadings.

The Missing Link: Bridging Laboratory Biorefineries to Industrial Implementation

2026-07-03T12:51:33+07:00

Artificial Intelligence Meets Catalysis: A New Approach to Heavy Diesel Desulfurization Using Trimetallic Activated Carbon Catalyst in Central Oscillating Reactor

2025-09-30T19:44:32+07:00

To meet the new strict environmental legislations about sulfur content in petroleum fuels and their harmful emissions for high quality fuels, deep desulfurization strategies, such as oxidation process, have become an interesting topic in the academic and industrial fields. Therefore, this work develops a new Trimetallic Activated Carbon (TAC) catalyst, which is synthesized by decorating activated carbon with magnetic-manganese active oxides and an alumina coating film, for continuous deep oxidative desulfurization (ODS). Furthermore, a novel central oscillating reactor (COR) is fabricated by developing central baskets, which are employed to pack catalyst particles for continuous oxidative desulfurization. This new design exhibits a practical solution for handling the solid catalytic materials in central basket baffles for continuous operation, compared to previous studies that utilized only a central baffle with dispersed catalyst particles in the reacting fluids through batch operation mode. Process efficiency is examined utilizing hydrogen peroxide oxidant under mild conditions: 1 atm, temperature (30–90) °C, liquid hour space velocity, LHSV (0.33–0.08) min^-1, oscillation parameters (amplitude, A: 3–12 mm, and frequency, f = 0.5–2 Hz). Also, Support Vector Machine (SVM) model is examined as a new machine learning tool to predict the desulfurization model. ODS technology shows high performance through low oxidation time (12 min) by reducing sulfur in heavy diesel fuel from 8281 ppm to 294 ppm to achieve 96.45 % oxidation efficiency under 90 °C, LHSV = 0.08 min^-1, A = 12 mm and f = 2 Hz. SVM model data performed excellent prediction at R² of 0.9962, mean absolute error (MAE) of 0.0791, and mean squared error (MSE) of 0.0078. The SVM strategy results in a high-accuracy artificial intelligence model under minimal deviations between actual and predicted data. The new integrated COR-TAC system provides an efficient, practical approach to deep, cost-effective and eco-friendly oxidative desulfurization; it can be directly scaled up into the refining industry at the same process performance.