Herein, a simple approach based on tailoring the surface charge of nanoparticles, NPs, during the preparation to boost the electrostatic attraction between NPs and the organic pollutant was investigated. In this study, chargeable titania nanoparticles (TiΟ₂ NPs) were synthesized via a hydrothermal route under different pH conditions (pH = 1.6, 7.0 and 10). The prepared TiΟ₂ NPs were fully characterized via various techniques including; transmission electron microscopy (TEM), X-ray diffraction (XRD), N₂ adsorption/desorption, X-ray photoelectron spectroscopy (XPS), Ultraviolet–visible spectroscopy (UV-Vis) and dynamic light scattering (DLS). The influence of the preparation pH on the particle size, surface area and band gap was investigated and showed pH-dependent behavior. The results revealed that upon increasing the pH value, the particle size decreases and lead to larger surface area with less particles agglomeration. Additionally, the effect of pH on the surface charge was monitored by XPS to determine the amount of hydroxyl groups on the TiO₂ NPs surface. Furthermore, the photocatalytic activity of the prepared TiΟ₂ NPs towards methylene blue (MB) photodegradation was manifested. The variation in the preparation pH affected the point of zero charge (pH_PZC) of TiO₂ NPs, subsequently, different photocatalytic activities based on electrostatic interactions were observed. The optimum efficiency obtained was 97% at a degradation rate of 0.018 min⁻¹ using TiO₂ NPs prepared at pH 10.

Recent advancements in analytical techniques have greatly contributed to the analysis of latent fingermarks’ (LFMs) “touch chemistry” and identification of materials that a suspect might have come into contact with. This type of information about the FM donor is valuable for criminal investigations because it narrows the pool of suspects. It is estimated that at least 30 million people around the world take over-the-counter and prescription nonsteroidal anti-inflammatory drugs (NSAIDs) for pain relief, headaches and arthritis every day. The daily use of such drugs can lead to an increased risk of their abuse. In the present study, Raman spectroscopy combined with multivariate statistical analysis was used for the detection and identification of drug traces in LFMs when NSAID tablets of aspirin, ibuprofen, diclofenac, ketoprofen and naproxen have been touched. Partial least squares discriminant analysis of Raman spectra showed an excellent separation between natural FMs and all NSAID-contaminated FMs. The developed classification model was externally validated using FMs deposited by a new donor and showed 100% accuracy on a FM level. This proof-of-concept study demonstrated the great potential of Raman spectroscopy in the chemical analysis of LFMs and the detection and identification of drug traces in particular.