We characterize the subsurface thermal degradation of an inert analog of high-explosive molecular crystals (Eu:Y(acac)3(DPEPO)) (EYAD) embedded inside of a plastic bonded explosive simulant using feedback-assisted wavefront shaping-based fluorescence and Raman spectroscopies. This technique utilizes wavefront shaping to focus pump light inside a heterogeneous material onto a target particle, which significantly improves its spectroscopic signature. We find that embedding the EYAD crystals in the heterogeneous polymer results in improved thermal stability, relative to bare crystal measurements, with the crystal remaining fluorescent to >612 K inside of the heterogeneous material, while the bare crystal's fluorescence is fully quenched by 500 K. We hypothesize that this improvement is due to the polymer restricting the effects of EYAD melting, which occurs at 400 K and is the primary mechanism for spectroscopic changes in the temperature range explored.

Ongoing technological advancements in the field of mid-infrared (MIR) spectroscopy continuously yield novel sensing modalities, offering capabilities beyond traditional techniques like Fourier transform infrared spectroscopy (FT-IR). One such advancement is MIR dispersion spectroscopy, utilizing a tunable quantum cascade laser and Mach-Zehnder interferometer for liquid-phase analysis. Our study assesses the performance of a custom MIR dispersion spectrometer at its current development stage, benchmarks its performance against FT-IR, and validates its potential for time-resolved chemical reaction monitoring. Unlike conventional methods of IR spectroscopy measuring molecular absorptions using intensity attenuation, our method detects refractive index changes (phase shifts) down to a level of 6.1 × 10-7 refractive index units (RIU). This results in 1.5 times better sensitivity with a sevenfold increase in analytical path length, yielding heightened robustness for the analysis of liquids compared to FT-IR. As a case study, we monitor the catalytic activity of invertase with sucrose, observing the formation of resultant monosaccharides and their progression toward thermodynamic equilibrium. Anomalous refractive index spectra of reaction mixtures, with substrate concentrations ranging from 2.5 to 25 g/L, are recorded, and analyzed at various temperatures, yielding Michaelis-Menten kinetics findings comparable to the literature. Additionally, the first-time application of two-dimensional correlation spectroscopy on the recorded dynamic dispersion spectra correctly identifies the mutarotation of reaction products (glucose and fructose). The results demonstrate high precision and sensitivity in investigating complex time-dependent chemical reactions via broadband refractive index changes.

The enhancement of Raman signals using photonic crystal structures has been the subject of numerous experimental and theoretical studies, leading to a variety of issues and inconsistencies. This paper presents a comprehensive experimental investigation into the impact of alignment between the laser excitation wavelength and the specific position of the photonic band gap on signal enhancement in Raman spectroscopy. By employing one-dimensional (1D) porous silicon photonic crystals, a systematic analysis across a large number of spectra was conducted. The study focused on examining the signal enhancement of both the Raman ∼520 cm-1 silicon band, representing the constituent material of photonic crystal, and the most prominent Raman bands of crystal violet, used as a probe molecule. The probe molecules were both infiltrated into and adsorbed on top of the photonic crystal structure. The obtained experimental results for the contribution of 1D photonic crystals to Raman signal enhancement are much smaller compared to most predictions. The Raman signal of silicon and the signal from the probe molecule are enhanced ≤2.5 times when the laser excitation aligns with the edge of the photonic band gap, strictly defined as the position at the very bottom of the reflectance peak. The results have been discussed within the context of theoretical explanations.

Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.

Using linear dichroism theory, one would assume that a z-cut of a uniaxial crystal is equivalent to an x-cut to determine the perpendicular component of the dielectric tensor and the corresponding oscillator parameters. However, Fresnel's equations show that the effect of interfaces in the form of the continuity relations of the different components of the electric field must be considered. A consequence of the continuity relations is that perpendicular modes increase less significantly in strength with increasing angle of incidence than expected. This is a consequence of the fact that it is the inverse of the perpendicular component of the dielectric function that increasingly becomes important with a growing angle of incidence. An inverse dielectric function, however, has typically much smaller values than the dielectric function. An additional consequence is that perpendicular modes are blueshifted and coupled in such a way that oscillator strength is transferred to the higher wavenumber mode. Thus, the spectral signatures of perpendicular modes are often weak and masked by the parallel modes when two modes overlap. Accordingly, to enable dispersion analysis, it is suggested to use a hybrid of the conventional residual sum of squares and the two-trace two-dimensional (2T2D) smart error sum, which can correct systematic multiplicable errors in the experimental spectrum. As demonstrated for fresnoite (Ba2TiSi2O8), this is an important step toward determining the perpendicular component of the dielectric tensor and the corresponding oscillator parameters using dispersion analysis, since asynchronous 2T2D correlation spectra are, in particular, sensitive to perpendicular modes.

Two-dimensional correlation spectroscopy is used to investigate the intermolecular interaction between two substances dissolved in the same solutions, where the intermolecular interaction is described by two reversible reactions producing two supramolecular aggregates. The severe overlappings expected among the characteristic peaks of the original solute and aggregates make conventional one-dimensional spectra difficult to accurately reflect the physiochemical nature of the intermolecular interaction. The double asynchronous orthogonal sample design (DAOSD) approach is utilized to analyze the simulated data for proof-of-principle demonstration. The patterns of cross-peaks are much more complex compared with the intermolecular interaction described by only a single reaction. Four major groups of cross-peaks with characteristic patterns observed in the pair of DAOSD asynchronous spectra are systematically analyzed and classified. Further analysis of the spectral feature of the cross-peaks of the DAOSD asynchronous spectra is helpful to exact additional information concerning the variation of the peak position and peak width of the aggregates compared with those of the original solute. The result is important to reveal the physicochemical nature of intermolecular interaction between the solutes (e.g., changes in conformation, dynamical behavior, etc.). The pattern of cross-peaks in the corresponding 2D asynchronous spectra may become rather complex when the peak position, peak width, and peak intensity of two supramolecular aggregates change simultaneously. Further work using artificial intelligence techniques to interpret the complex cross-peaks is still being carried out.

In this work, we present studies on the effect of laser wavelengths on the laser-induced plasma characterization using a femtosecond (fs) ytterbium-doped potassium-gadolinium tungstate (Yb:KGW) laser. Plasma plumes of copper, steel, ceramics, and glass samples were induced using a multiple shot of 200 fs laser pulses with 1030 nm and 343 nm wavelengths at fixed laser fluence () and analyzed using the laser-induced breakdown spectroscopy (LIBS) technique. Time-resolved fs-LIBS measurements were performed on the same set of samples and under the same experimental conditions. For the calculation of plasma parameters, the set of spectral lines of Cu(I) (for copper sample), Fe(I) (for steel sample), and Ca(I), K(I) (for glass and ceramics samples) were observed. The plasma electron temperature and density were evaluated from the Boltzmann plots and Stark-broadening profiles of the plasma spectral lines, assuming the local thermodynamic equilibrium condition. Time-resolved plasma temperature and electron density for fs-LIBS using ultraviolet (UV) and infrared (IR) laser wavelengths were analyzed and no significant dependence on fs laser wavelength was observed for any of the samples. However, for all samples the signal-to-noise ratio (SNR) significantly increased using UV laser radiation: copper (), steel (), glass (), and ceramics (). Therefore, by using a fs UV laser wavelength for laser-induced breakdown spectroscopy experiments, for certain materials the SNR and at the same time the limit of detection can be significantly enhanced.

Many optical applications, including free-space optical communications, lidar, and astronomical measurements, are impacted by the presence of light-scattering particles also known as obscurants. Scattering from particles consisting of sand, dust, dirt, and other substances can significantly degrade optical signals. For many obscurants, the index of refraction is dependent on the wavelength of light, and there exists a Christiansen wavelength (λc) at which scattering is at a minimum. At λc the index of refraction of the scattering particles (ns) matches that of the surrounding medium, in this case air (with refractive index na). This condition makes the scattering particulates almost invisible to the propagating light, minimizing scattering and increasing transmission at λc. Previously, the authors showed a technique for measuring the index of refraction n(λ) and the extinction coefficient k(λ) using spectroscopic ellipsometry for various sand samples. Spectroscopic measurements on static sand samples demonstrated good agreement with the predicted spectral properties and highlighted the presence of a Christiansen feature near 8 µm. However, in outdoor environments, the scattering particles are never stationary but in a constant state of motion. In this work, spectroscopic measurements on dynamic sand samples (sand that is falling through the optical beam path) show two Christiansen features seen previously in predicted and observed static sand measurements. Additionally, we characterize, for the first time, transmission around a Christiansen feature using a tunable laser and show results consistent with other spectroscopic measurements.

The precise regulation of nanoenzyme activity is of great significance for application to biosensing analysis. Herein, the peroxidase-like activity of carbon dots was effectively modulated by doping phosphorus, which was successfully employed for sensitive, selective detection of acid phosphatase (ACP). Phosphorus-doped carbon dots (P-CDs) with excellent peroxidase-like activity were synthesized by a one-pot hydrothermal method, and the catalytic activity could be easily modulated by controlling the additional amount of precursor phytic acid. P-CDs could effectively catalyze the oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to blue TMB oxidation products in the presence of hydrogen peroxide. While ACP was able to catalyze the hydrolysis of L-ascorbyl-2-phosphate trisodium salt (AAP) to produce ascorbic acid (AA), which inhibited the peroxidase-like activity of P-CDs, by combining P-CDs nanoenzymes and ACP-catalyzed hydrolysis the colorimetric method was established for ACP detection. The absorbance variation showed a good linear relationship with ACP concentration in the range of 0.4-4.0 mU/mL with a limit of detection at 0.12 mU/mL. In addition, the method was successfully applied to detect ACP in human serum samples with recoveries in the range of 98.7-101.6%. The work provides an effective strategy for regulating nanoenzymes activity and a low-cost detection technique for ACP.

Laser-induced plasmas of materials containing hydrocarbons present strong carbon molecular emission features. Using these emissions to build models relating changes in spectral features to a physical parameter of the system, such as hydrocarbon content, can be difficult because of the dynamic complexity of the spectral features and temperature disequilibrium between molecular species. This study presents machine learning models trained to quantify the mole fraction of hexane in hexane-air plasmas from CN Violet and C2 Swan spectral features. Ensemble regression methods provide the most accurate predictions with root mean squared error on the order 10-2. Artificial neural network regressions produce predictions with superlative sensitivity, exhibiting detection limits as low as 0.008. These foundational models can be further refined with more advanced data to quantify the presence of carbon species in complex plasma environments, such as high-speed reacting flows.