Characterization of Two BAHD Acetyltransferases Highly Expressed in the Flowers of Jasminum sambac (L.) Aiton

Volatile benzenoid compounds are found in diverse aromatic bouquets emitted by most moth-pollinated flowers. The night-blooming Jasminum sambac is widely cultivated worldwide in the tropics and subtropics for ornamental and industrial purposes owing to its fragrant flowers. Benzylacetate is a characteristic constituent in jasmine scent which makes up to approximately 20–30% of the total emission in the headspace or extract, but the biosynthesis enzymes and the encoding genes have not yet been described. Here, we identify two cytosolic BAHD acyltransferases specifically expressed in the petals with a positive correlation closely to the emission pattern of the volatile benzenoids. Both JsBEAT1 and JsBEAT2 could use benzylalcohol and acetate-CoA as substrates to make benzylacetate in vitro. The recombinant GST-JsBEAT1 has an estimated apparent Km of 447.3 μM for benzylalcohol and 546.0 μM for acetate-CoA, whereas in the instance of the His-JsBEAT2, the Km values are marginally lower, being 278.7 and 317.3 μM, respectively. However, the catalytic reactions by the GST-JsBEAT1 are more efficient than that by the His-JsBEAT2, based on the steady-state kcat parameters. Furthermore, ectopic expression of JsBEAT1 and JsBEAT2 in the transgenic P. hybrida plants, driven by a flower-specific promotor, significantly enhances the biosynthesis of benzylbenzoate and benzylacetate, as well as the total VOCs.

Unidirectional emission of phase-controlled second harmonic generation from a plasmonic nanoantenna

Shaping the emission pattern of second harmonic (SH) generation from plasmonic nanoparticles is important for practical applications in nonlinear nanophotonics but is rendered challenging by the complex second-order nonlinear-optical processes. Here, we theoretically and experimentally demonstrate that a pair of V- and Y-shaped gold nanoparticles directs the SH emission perpendicularly to an incident light direction. Owing to spatial overlap of two orthogonal plasmonic dipole modes at the fundamental and SH wavelengths of the individual particles, surface SH polarizations induced by the fundamental field is efficiently near-field coupled to the SH plasmon mode, resulting in dipolar SH emission from the individual particles. Moreover, the phase of this emission can be tuned simply by altering the part of the Y-particle shape, which changes the SH plasmon resonance while keeping the fundamental resonance. Our approach is a promising platform for engineering not only directional nonlinear nanoantennas but also nonlinear metamaterials.

Moisture-Induced Pattern of Gases and Physicochemical Indices in Corn Straw and Cow Manure Composting

This study investigated the altering effect of moisture on the emission pattern of gases and the evolutionary dynamics of physicochemical indices in corn straw and cow manure composting. Exploring this effect was reasonable to unravel the use of moisture as a cheap alternative to control gaseous emissions and improve the final properties of compost. The nutrient dynamics of the compost showed 21.6% losses in total organic carbon content, with a 33.3% increase in total nitrogen content at the end of composting. All the gases (CH4, CO2, N2O and NH3) yielded a common emission pattern despite the differences in moisture content. Except for CH4, the peak and stable emission periods of all the gases were observed on the 5th day (thermophilic phase) and after the 27th day (late mesophilic phase) of composting, respectively. Emission reductions of 89%, 91%, 95% and 100% were recorded for CH4, CO2, N2O and NH3, respectively, during the late mesophilic phase of composting. From the study, the 65% moisture content was efficient in reducing the loss rate of the gasses and nutrient contents of the compost. This study would enable farmers to channel organic residues generated into compost while minimizing pollution and nutrient losses associated with the composting process.

Emission Pattern of Compression Ignition Engine Fueled with Blends of Tropical Almond Seed Oil-Based Biodiesel using Artificial Neural Network

Engine pollutants have been a significant source of concern in most countries around the world because they are one of the major contributors to air pollution, which causes cancer, lung disorders, and other severe illnesses. The need to reduce emissions and its consequences has prompted studies into the emission profile of internal combustion engines running on particular fuels. To this end, this study employed the power of Artificial Neural Networks (ANNs) to investigate the impact of injection timing on the emission profile of Compression Ignition Engines fuelled with blends of Tropical Almond Seed Oil based-biodiesel; by conducting a series of experimental tests on the engine rig and using the results to train the ANNs; to predict the emission profile to full scale. Blend percentages, load percentages, and injection timings were used as input variables, and engine emission parameters were used as output variables, to train the network. The results showed that injection timing affect emission output of CI engines fuelled with Tropical Almond Oil based biodiesel; and for the emission pattern to be friendly, injection timing must rather be retarded and not advanced. The results also showed that for different engine emission parameters, there is a strong association between the ANN output results and the actual experimental values; with mean relative error values less than 10%, which fall within the acceptable limits. For emission of CI engines fuelled with Tropical Almond Oil based biodiesel to be friendly in pattern, injection timing must be relatively retarded. The study also concluded that Artificial Neural Network (ANN) is a reliable tool for predicting Compression Ignition Engines emission profiles.