Low ice adhesion anti-icing coatings based on PEG release from mesoporous silica particles loaded SBS

Release-based extremely low ice adhesion strength and durable anti-icing coatings were designed and realized by loading mesoporous silica particles (MSP) into SBS polymer matrix and filling poly(ethylene glycol) (PEG) as the anti-icing agent into MSP/SBS composites. This approach allows the formation of a thin lubricating liquid layer of PEG and water at the ice/composite interface at sub-zero temperatures and results in ice adhesion strengths as low as 3 kPa. The high specific surface area of MSP (428 m2 g-1) as the anti-icing agent carrier significantly contributed to the retainment of PEG in the composites. The freezing time of water droplets on the composites increased and the ice adhesion strength decreased with the amount of PEG retained in the composites. After 15 icing/deicing cycles, the ice adhesion strength was measured to be ~5 kPa indicating that a rather slow release (and removal with ice) of PEG at -10 °C from surface-exposed pores of MSP. The importance of PEG at the ice/composite interface was confirmed by ice adhesion strength measurements of frozen PEG-containing aqueous solutions on unfilled MSP/SBS composites. These results clearly show that PEG filled MSP/SBS composites demonstrate a passive anti-icing mechanism based on sustained release of PEG with extremely low ice adhesion strength and significant potential for longer-term use in sub-zero temperatures and harsh environments

Formation of mesoporous silica particles with hierarchical morphology

The transformation of mesoporous silica morphology from monoliths to spherical particles was investigated at room temperature in Pluronic F127/TEOS system as a function of HCl acid catalyst concentration to understand and control the mechanism. It is shown that the specific surface area and the size of mesoporous spherical silica particles can simply be adjusted by the catalyst concentration without using any additives or post-treatment. Above 3 M acid concentration, novel monodisperse micron sized spherical silica with hierarchical order of two levels was obtained. These silica spheres were formed of densely packed distorted hexagonal platelets of 20-30 nm in diameter. Within these platelets mesoporous channels were oriented along a single direction, however the platelets were randomly oriented in the spherical particles. Controlling the agglomeration of mesoporous silica primary particles by the concentration of the acid catalyst to obtain micron-sized spherical particles is novel. This approach allows the synthesis of particles whose sizes can be controlled in the range of similar to 1-4 mu m and specific surface area in the range of similar to 200-500 m(2)/g. The morphology of the particles transforms from spherical shape to mesoporous monoliths at acid concentrations below 1 M due to slow hydrolysis and condensation. These results are important in understanding the role of catalyst concentration on the formation mechanism of different morphologies of mesoporous silica

Regression Modeling Strategies to Predict and Manage Potato Leaf Roll Virus Disease Incidence and Its Vector

The potato leaf roll virus (PLRV) disease is a serious threat to successful potato production and is mainly controlled by integrated disease management; however, the use of chemicals is excessive and non-judicious, and it could be rationalized using a predictive model based on meteorological variables. The goal of the present investigation was to develop a disease predictive model based on environmental responses viz. minimum and maximum temperature, rainfall and relative humidity. The relationship between epidemiological variables and PLRV disease incidence was determined by correlation analysis, and a stepwise multiple regression was used to develop a model. For this purpose, five years (2010–2015) of data regarding disease incidence and epidemiological variables collected from the Plant Virology Section Ayub Agriculture Research Institute (AARI) Faisalabad were used. The model exhibited 94% variability in disease development. The predictions of the model were evaluated based on two statistical indices, residual (%) and root mean square error (RMSE), which were ≤±20, indicating that the model was able to predict disease development. The model was validated by a two-year (2015–2017) data set of epidemiological variables and disease incidence collected in Faisalabad, Pakistan. The homogeneity of the regression equations of the two models, five years (Y = −47.61 − 0.572x1 + 0.218x2 + 3.78x3 + 1.073x4) and two years (Y = −28.93 − 0.148x1 + 0.510x2 + 0.83x3 + 0.569x4), demonstrated that they validated each other. Scatter plots indicated that minimum temperature (5–18.5 °C), maximum temperature (19.1–34.4 °C), rainfall (3–5 mm) and relative humidity (35–85%) contributed significantly to disease development. The foliar application of salicylic acid alone and in combination with other treatments significantly reduced the PLRV disease incidence and its vector population over control. The salicylic acid together with acetamiprid proved the most effective treatment against PLRV disease incidence and its vector M. persicae

Predicting Stripe Rust Severity in Wheat Using Meteorological Data with Environmental Response Modeling

Objective: The main objective of current investigation was to develop a predictive disease model based upon meteorological data, viz., maximum temperature, minimum temperature, rainfall, relative humidity, and wind speed to predict stripe rust severity (%). Methods: Five years' data of stripe rust severity on three wheat varieties, namely SA-42, Sandal-73, and Barani-70, continuously cultivated for five years (2013–2017), were collected from experimental trials of Deputy Director of Agriculture Extension Layyah to develop a predictive disease model. For validation of the model, a research trial was conducted in the Research Area of the Department of Plant Pathology, Bahadar Sub-Campus Layyah, during the crop seasons of 2018–2019, following procedures similar to those utilized in five years investigation. The data on epidemiological variables used in the present investigation was collected from the Pakistan Meteorological Observatory at Karor-Layyah. To evaluate the association between meteorological factors and disease severity correlation and regression analysis was performed. Results: All meteorological variables contributed significantly in disease development and showed 89 % variability in stripe rust severity (%). Root means square error (RMSE) and residual (%) were used to evaluate the model's predictions. Both indices were below 20, showing that the model could accurately predict the progression of disease. The regression equations of 5 years model (Y = -63.11 + 0.96x1 + 1.72x2 + 3.72x3 + 0.43x4) and 2 years model (Y = -40.2 + 1.80x1 + 1.18x2 + 2.29x3 + 0.39x4) validated each other. Scatter plots indicated that environmental factors such as maximum temperature (12.8–22.5 °C), minimum temperature (8.7–14.8 °C), relative humidity (50–85 %), and wind speed (1.3–4.5) influenced the progression of stripe rust epidemic. Conclusion: Understanding the epidemiology of stripe rust will help us to forecast its progression, allowing wheat growers to more precisely adapt plant protection measures