Nanogap-Rich Surface-Enhanced Raman Spectroscopy-Active Substrate Based on Double-Step Deposition and Annealing of the Au Film over the Back Side of Polished Si

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive and rapid detection technique that is used for detection of various analytes in trace quantities. We present a sensitive, large-area, and nanogap-rich SERS-active substrate by altering a thin gold (Au) film on the unpolished side of a single-side polished silicon wafer by repeated thermal deposition and annealing in an argon environment. The repeated thermal deposition and annealing process was compared on both sides of a one-side-polished silicon wafer; however, the rear side (etched/unpolished side) demonstrated a more enhanced Raman signal owing to the larger effective area. The proposed substrate can be fabricated easily, having a high density of hotspots distributed uniformly all over the substrate. This ensures easy, rapid, and sensitive detection of analytes with a high degree of reproducibility, repeatability, and acceptable uniformity. The optimized substrate shows a high degree of stability with time when exposed to the ambient environment for a longer duration of 148 days. The reported substrate can detect up to 10-11 M concentrations of 2,4,6-trinitrotoluene (TNT) and 2,4-dinitrotoluene (DNT), with limits of detection (LODs) of 1.22 and 1.26 ng/L, respectively. This work not only presents the efficient and sensitive SERS-active substrate but also shows the advantages of using the rear side of a one-side-polished silicon substrate as a SERS-active chip.

Recruiting Perovskites to Degrade Toxic Trinitrotoluene

Everybody knows TNT, the most widely used explosive material and a universal measure of the destructiveness of explosions. A long history of use and extensive manufacture of toxic TNT leads to the accumulation of these materials in soil and groundwater, which is a significant concern for environmental safety and sustainability. Reliable and cost-efficient technologies for removing or detoxifying TNT from the environment are lacking. Despite the extreme urgency, this remains an outstanding challenge that often goes unnoticed. We report here that highly controlled energy release from explosive molecules can be accomplished rather easily by preparing TNT-perovskite mixtures with a tailored perovskite surface morphology at ambient conditions. These results offer new insight into understanding the sensitivity of high explosives to detonation initiation and enable many novel applications, such as new concepts in harvesting and converting chemical energy, the design of new, improved energetics with tunable characteristics, the development of powerful fuels and miniaturized detonators, and new ways for eliminating toxins from land and water.

Classical Least Squares-Assisted Mid-Infrared (MIR) Laser Spectroscopy Detection of High Explosives on Fabrics

Mid-infrared (MIR) laser spectroscopy was used to detect the presence of residues of high explosives (HEs) on fabrics. The discrimination of the vibrational signals of HEs from a highly MIR-absorbing substrate was achieved by a simple and fast spectral evaluation without preparation of standards using the classical least squares (CLS) algorithm. Classical least squares focuses on minimizing the differences between the spectral features of the actual spectra acquired using MIR spectroscopy and the spectral features of calculated spectra modeled from linear combinations of the spectra of neat components: HEs, fabrics, and bias. Samples in several combinations of cotton fabrics/HEs were used to validate the methodology. Several experiments were performed focusing on binary, ternary, and quaternary mixtures of TNT, RDX, PETN, and fabrics. The parameters obtained from linear combinations of the calculated spectra were used to perform discrimination analyses and to determine the sensitivity and selectivity of HEs with respect to the substrates and to each other. However, discrimination analysis was not necessary to achieve successful detection of HEs on cotton fabric substrates. The RDX signals ( m_RDX > 0.02 mg) on cotton were used to calculate the limit of detection (LOD). The signal-to-noise ratios (S/N) calculated from the spectra of cotton dosed with decreasing masses of RDX until S/N ≈ 3 resulted in a LOD of 15-33 µg, depending on the vibrational band used. Linear fits generated by comparing the mass dosed RDX with the fraction predicted were also used to calculate the LOD based on the uncertainty of the blank and the slope. This procedure resulted in a LOD of 58 µg. Probably the most representative value of the method LOD was calculated using an interpolation of a threshold determined using the predicted average value for the blank plus 3.28 times the standard deviations ( p-value threshold) for low surface dosages of RDX (LOD = 40 µg). The contribution demonstrates that to achieve HE detection on fabrics using the proposed algorithm, i.e., determining the presence/absence of HEs on the substrates, the library must contain the spectra of HEs, substrates, and potential interferents or that these spectra be added to the models in the field. If the model does not contain the spectra of the fabric components, there is a high probability of finding false positives for clean samples (no HEs) and a low probability for failed detection in samples with HEs. More work will be required to demonstrate that these new approaches to HE detection work on real-world samples and when contaminating materials are present in the samples.