Rapid detection of nerve agents from complex matrices with minimal sample preparation is essential due to their high toxicity and bioavailability. In this work, quantum dots (QDs) were functionalized with oligonucleotide aptamers that specifically targeted a nerve agent metabolite, methylphosphonic acid (MePA). These QD-DNA bioconjugates were covalently linked to quencher molecules to form Förster resonance energy transfer (FRET) donor–acceptor pairs that quantitatively measure the presence of MePA. Using the FRET biosensor, the MePA limit of detection was 743 nM in artificial urine. A decrease in the QD lifetime was measured upon DNA binding and was recovered with MePA. The biosensor’s flexible design makes it a strong candidate for the rapid detection of chemical and biological agents for deployable, in-field detectors.

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, $0.1\le C\le 2.0$ and $0\le \alpha \le 4$, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values $C=2.0$ and $\alpha =0.0736$. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, $R^2$, greater than $0.97$, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

The self-activated cesium hafnium chloride (CHC) scintillator with cubic crystal structure is attracting tremendous attention due to its various advantages. Being self-activated, CHC offers uniform scintillator properties along the entire volume of the grown ingots irrespective of their sizes. The added advantage is excellent non-proportionality compared to other inorganic scintillators. There are wide variations of energy resolution values reported in the literature, the variations might be due to the different purity of the starting materials. In recent years, cesium zirconium chloride (CZC) scintillator and mixed scintillators with CHC are also gaining interest, as the atomic radius of Hf and Zr are equal. However, very limited information is available on the scintillator properties of CZC and CHZC in the literature. In this presentation, we will discuss the growth and characterization of CHC and CHZC scintillators grown by the vertical Bridgman technique. Our study shows the partial replacement of Hf by Zr improves the energy resolution compared to CHC grown by same growth parameters and purity of the starting material.