RESULTS: Scanning electron microscopy images demonstrated successful attachments of NBR onto the constituents of fingerprints on the substrates. The highest average quality of visualised fingerprints was attained at the optimum condition (100 mg of CRL; 75 mg of acid-functionalised multi-walled carbon nanotubes; 5 h of immobilisation). The NBR produced comparable average quality of fingerprints with the commercially available small particle reagent, even after 4 weeks of storage (without any preservatives) in both chilled and sultry conditions. The NBR was sensitive enough to visualise the increasingly weaker fingerprints, particularly on glass slides.

METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior.

RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region.