Prediction of ln ODT.

Polynomial regression analysis with 10-fold cross-validation to predict ln ODT from physicochemical measures utilising median ODT values from 1,274 volatile chemicals.</p

Prediction of cannabis sensory descriptors (SD) from volatile data, and OI data produced through odour vector modelling.

Logistic regression analysis and k-means unsupervised cluster analysis (k = 2) to predict the SD of 265 cannabis samples. Data represent the percentage of strains correctly assigned for each SD (for each data type), with error bars representing the 95% confidence interval. A) the set of 6 SDs which were predicted significantly more accurately with OI than terpene data, B) the set of 5 SDs which were predicted with equal accuracy for both OI and terpene data, and C) the single SD which was predicted significantly more accurately with terpene than OI data. Significant differences between volatile and OI data for an individual SD are indicated by * for p < 0.05, and *** for p ≤ 0.001 calculated by Student’s t-test.</p

S1 File -

Appendix 1. Methods and equations for odour vector modelling containing Equations S1-S8, Appendix 2. Table S1-S8 and Fig S1, and Appendix 3. Equations for prediction of SD containing Equations S9-S31. (DOCX)</p

Overview of methods workflow.

Cannabis flowers volatile profiles were converted into sample odour intensity (OI) profiles via vector modelling, which utilised odour descriptor (OD) data, odour detection thresholds (ODT), and compound concentrations as calculation inputs prior to vector addition. Sample OI profiles were compared to sensory data which allowed for logistic regression and k-means clustering to predict sensory descriptor assignment.</p

Superhydrophobicity from the Inside

The nature of trapped air on submersed ultra-water-repellent interfaces has been investigated. These gaseous layers (plastrons) can last from hours to, in some examples such as the <i>Salvinia molesta</i> fern, months. The interface of submerged superhydrophobic surfaces with carefully controlled micropatterned surface roughness has been probed using synchrotron-based high-resolution X-ray phase tomography. This technique looks in situ, through the aqueous/gas interface in three dimensions. Long-term plastron stability appears to correlate with the appearance of scattered microdroplets <20 μm in diameter that are sandwiched within the 30 μm thick gaseous interfacial layer. These microdroplets are centered on defects or damaged sections within the substrate surface approximately 20–50 μm apart. Such irregularities represent heterogeneous micro/nano-hierarchical structures with varying surface structures and chemistry. The stability of microdroplets is governed by a combination of electrostatic repulsion, contact angle limitations, and a saturated vapor pressure, the latter of which reduces the rate of diffusion of gas out of the air layer, thus increasing underwater longevity. Homogenous surfaces exhibiting purely nano- or micro-regularity do not support such microdroplets, and, as a consequence, plastrons can disappear in <20 h compared with >160 h for surfaces with scattered microdroplets. Such behavior may be a requirement for long-term nonwetting in any system