Applied Spectroscopy最新文献

An absorption imaging technique was described for visualizing molybdenum pentachloride (MoCl₅) flow during an atomic layer deposition/pulsed chemical vapor deposition process. The imaging system was composed of a telecentric lens and a commercial 7.1-megapixels (MP) complementary metal oxide semiconductor (CMOS) camera. The light source was a fiber-coupled light emitting diode operating at a peak emission wavelength of 443  nm. Flow images of MoCl₅ vapor entrained in a carrier gas were recorded at approximately 93 frames per second in a research-grade vapor deposition chamber. The utility of this technique was illustrated by comparing the MoCl₅ flow patterns for two precursor injection conditions, conditions consisting of different argon carrier gas flow rate and chamber pressure. For a low flow rate and chamber pressure, the flow images showed a gradual expansion of the MoCl₅ concentration front through the field of view with a relatively short MoCl₅ residence time. These flow patterns result in a relatively uniform precursor concentration front impinging on the wafer surface with the precursor being efficiently exhausted from the chamber, making these conditions desirable for thin film deposition in this chamber. For a high carrier gas flow rate and elevated chamber pressure, the flow images showed a high gas velocity jet impinging on the wafer chuck surface and the formation of gas recirculation zones, resulting in a relatively long residence time. These flow conditions would make it difficult to reproducibly deposit uniform thin films in this chamber. This comparison demonstrated the utility of this technique for qualitative characterization of precursor flow fields with minimal data processing. However, the two-dimensional data obtained from this technique can also provide the basis for training and validating computational fluid dynamics models. Furthermore, the addition of duplicate optical systems would provide the basis for determining the three-dimensional precursor distribution through tomographic analysis.

Diabetes, a chronic metabolic disorder affecting millions worldwide, presents a persistent need for reliable and non-invasive diagnostic techniques. Here, we suggest a highly effective approach for differentiating between fingernails from diabetic individuals and those from healthy controls using laser-induced breakdown spectroscopy (LIBS). The excitation source employed was a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser emitting light with a wavelength of 1064  nm. The initial differentiation between individuals with and without diabetes was achieved by applying principal component analysis (PCA) to LIBS spectral data, which was then incorporated into a novel machine-learning model. The classification model designed for a non-invasive system included random forest (RF), an extreme learning machine (ELM) classifier, and a hybrid classification model incorporating cross-validation techniques to evaluate the outcomes. The algorithm analyses the complete spectrum of both healthy and diseased samples, categorizing them according to differences in LIBS spectral intensity. The classification performance of the model was assessed using a k-fold cross-validation method. Seven parameters, i.e., specificity, sensitivity, area under curve (AUC), accuracy, precision, recall, and F-score, were used to evaluate the model's overall performance. The findings affirmed that the suggested non-invasive model could predict diabetic diseases with an accuracy of 95%.

The measurement of thin films with a thickness in the nanometer range is challenging because it requires extensive sample preparation, vacuum condition, long measurement times or using test inks that additionally contaminate the surface. The detection of those films is crucial for production processes that rely on a boundary layer to create a proper interface like adhesive bonding, coating, or lithography in various industries like automotive, solar, energy storage and semiconductor manufacturing. Consequently, there is a need for quick, reliable measurement techniques with high sensitivity to ensure the technical cleanliness of the opaque surface. In this paper the feasibility of epi-detection with coherent Raman scattering (CRS) Imaging is investigated on different substrate materials and demonstrated to be a method for fast scanning of large nontransparent surfaces including chemical fingerprinting of the substances atop. Therefore, various samples with low surface energy filmic contaminations from polysiloxanes are produced and investigated with CRS Imaging, a technique mostly applied to biological samples with the novel use demonstrated here for surface contamination monitoring in material sciences.

Deep eutectic solvents (DESs) exhibit dynamic heterogeneity, where the intricate and dynamic hydrogen bonding within the DES mediates dynamic spatial variation in the DES local environment. The Type III DES composed of choline chloride and glycerol (ChCl:Gly) exhibits this effect prominently, and we report on the observed local organization and its dependence on system composition using the time-resolved reorientation dynamics of three illustrative chromophores of different polarities: perylene (neutral, nonpolar), oxazine 725 (cation, polar) and rose bengal (dianion, polar). Our findings demonstrate that the environments sensed by all three chromophores are markedly different than that predicted by the bulk viscosity of the DES, and that these local environments exhibit remarkably little change as the mole ratio of the DES constituents is varied. Taken collectively, these data provide clear evidence of short-range organization that bears very little resemblance to the longer-range structural organization that determines DES bulk properties.

This study demonstrates the feasibility of determining soil provenance from tree ash composition using elemental analysis and chemometric techniques. To date, no published studies have applied chemometric approaches to classify ash for provenance determination following forest fires. In this work, Pinus ponderosa ash was analyzed to distinguish samples based on soil type and geographic location. Pinus ponderosa, a widely distributed pine species in the western United States where wildfires are prevalent, was selected as a model system. Needles were collected from trees grown in five distinct soil types across northern Arizona and Colorado, then dry-ashed under controlled conditions. Classification was performed using three preprocessing techniques and five machine learning algorithms, including hierarchical modeling structures to optimize separation. Partial least squares discriminant analysis (PLS-DA) following a Box-Cox transformation yielded the highest classification accuracy, achieving a prediction kappa value of 0.98 for soil type identification. However, classification performance decreased when distinguishing both soil type and geographic location, indicating that additional variability may influence predictive accuracy in broader applications. These findings highlight the potential of inductively coupled plasma mass spectrometry (ICP-MS) and machine learning for post-wildfire forensic analysis and environmental monitoring.

Crystallization from the melt is a critical process governing the properties of semi-crystalline polymeric materials. While structural analyses of melting and crystallization transitions in bulk polymers have been widely reported, in contrast, those in thin polymer films on solid supports have been underexplored. Herein, in situ Raman microscopy and self-modeling curve resolution (SMCR) analysis are applied to investigate the temperature-dependent structural changes in poly(ethylene oxide) (PEO) films during melting and crystallization phase transitions. By resolving complex overlapping sets of spectra, SMCR analysis reveals that the thermal transitions of 50 µm thick PEO films comprise two structural phases: an ordered crystalline phase and a disordered amorphous phase. The ordered structure of the crystalline PEO film entirely disappears as the polymer is heated; conversely, the disordered structure of the amorphous PEO film reverts to the ordered structure as the polymer is cooled. Broadening of the Raman bands was observed in PEO films above the melting temperature (67 °C), while sharpening of bands was observed below the crystallization temperature (45 °C). The temperatures at which these spectral changes occurred were in good agreement with differential scanning calorimetry (DSC) measurements, especially during the melting transition. The results illustrate that in situ Raman microscopy coupled with SMCR analysis is a powerful approach for unraveling complex structural changes in thin polymer films during melting and crystallization processes. Furthermore, we show that confocal Raman microscopy opens opportunities to apply the methodology to interrogate the structural features of PEO or other surface-supported polymer films as thin as 2 µm, a thickness regime beyond the reach of conventional thermal analysis techniques.

To better interpret the Raman spectra from mammalian cells, it is often desirable to reduce their complexity by decomposing them into the spectral contributions from individual macromolecules or types of macromolecules. Diverse methods exist for demixing complex spectra, each with different benefits and drawbacks. However, some methods require a library of component spectra that might not be available, while others are hampered by noise and peak congestion that includes many proximal overlapping peaks. Through rapid fitting of individual peaks in every spectrum of a Raman hyperspectral data set, we have obtained individual peak parameters from which we determined the trends for all the peak amplitudes. We then grouped similar trends with k-means clustering. Then we used the peak parameters of all the peaks in a given cluster to reconstruct a spectrum representative of that cluster. This method produced spectra that were less distorted by unrelated overlapping peaks or noise, were less congested than those in the hyperspectral set, and thereby improved peak identification and macromolecule recognition. We have demonstrated the application of the method with Raman spectra from a perchlorate-polystyrene model system and extended it to complex spectra from methanol-fixed mammalian cells. We were able to recover independent spectra of perchlorate and polystyrene in the model system and spectra pertaining to individual macromolecular types (proteins, nucleic acids, lipids) from the mammalian cell data. We discuss how imperfections in spectral preprocessing and peak fitting can adversely affect the results. In summary, we have provided a proof-of-concept for a novel mixture resolution method with different attributes than extant ones.

Surface immobilization of DNA for biosensing or separations applications requires covalent attachment chemistry that is efficient, reproducible, and stable. In this work, an approach to link thiol-functionalized DNA to thiol-modified silica surfaces using N,N'-1,4-phenylene-bismaleimide is optimized by developing an efficient, one-pot synthesis of the maleimide-conjugated DNA followed by its immediate reaction with thiolated porous silica particles. The methodology takes advantage of a Michael addition reaction that couples a phenyl-bismaleimide cross-linking reagent and thiol-modified DNA to form a monomeric DNA-maleimide conjugate. The 1:1 stoichiometry of this reaction must be carefully controlled to avoid excess thiol-DNA, which generates unreactive bismaleimide-linked DNA dimers, or excess bismaleimide, which competes with the DNA-maleimide conjugate for reaction with the thiolated silica surface. To achieve control over the reaction forming the DNA conjugate, we adapt a fluorescence assay for free-thiols using 7-diethylamino-3-(4-maleimidophenyl)-4-methyl-coumarin (CPM) to determine the concentration of thiol-modified DNA that emerges from its synthesis, disulfide labeling, reduction to a thiol, and purification. The fluorescence response of the CPM reagent was calibrated using reduced glutathione as a standard, which allowed determination of the concentrations of thiolated-DNA and control over the stoichiometry of its reaction with a bismaleimide linker. The maleimide-conjugated DNA product thus formed was then reacted with thiolated-silica in order to bind the DNA to the internal surfaces of porous silica, whose surface populations were determined in individual particles by confocal Raman microscopy. Self-modeling curve resolution of the Raman spectra of surface-bound molecules validated the efficiency of the bismaleimide:thiolated DNA reaction, which provided stoichiometric control over formation of the monomeric DNA-maleimide conjugate and its optimized reaction with thiolated-silica surfaces.

Physiological systems are not at equilibrium and undergo time-dependent fluctuations, making it challenging to relate in vitro studies to in vivo biomolecular behavior. To bridge this gap, enzyme dynamics can be studied in the presence of controlled perturbations that recapitulate the intracellular environment. Here, we report an approach to the study of reactive oxygen species (ROS) based on the in situ manipulation of hydrogen peroxide (H₂O₂) levels in functionalized nanopore-based electrochemical zero-mode waveguide (EZMW) arrays, with each nanopore presenting small numbers of immobilized horseradish peroxidase (HRP) enzyme molecules. H₂O₂ is generated or consumed within the attoliter volume of the EZMW nanopores by poising an embedded ring electrode to suitable potentials, and the resulting effect on apparent turnover of HRP under non-equilibrium conditions is monitored using the enzymatically accelerated conversion of the non-fluorescent probe Amplex Red to fluorescent resorufin. A Nafion membrane is placed on the top surface of the EZMW array, providing a cation permselective barrier to transport in, or out, of the EZMW nanopores, thereby improving the sensitivity of the experiment by sequestering enzymatically generated resorufin in the attoliter volume of the EZMW nanopores. By fabricating arrays presenting 441 individual reaction volumes in parallel, distinct changes in population dynamics in the presence of in situ H₂O₂ generation or consumption are characterized with respect to temporal evolution and magnitude of the H₂O₂ aliquot delivered. This approach presents a promising avenue for studying biomolecular reactions in spatiotemporally controlled chemical environments that can mimic the non-equilibrium conditions encountered in vivo.