Applied Spectroscopy最新文献

In this study, we have developed a plasmonic hybrid heterostructure integrating two elements: Two-dimensional (2D) reduced graphene oxide-gold nanostars composite (rGO-GNS), and gold nanostars (GNS) substrate. By harnessing the unique plasmonic properties of rGO in chemical enhancement and that of GNS in electromagnetic enhancement, the hybrid heterostructure offers synergistic enhancement effects that enable ultra-low sensitivity and accurate identification and analysis of trace quantities of target substances. It is noteworthy that the high-density hotspots generated by strong plasmonic coupling of rGO-GNS and GNS results in ultra-high surface-enhanced Raman spectroscopy (SERS) enhancement compared to individual substrate either GNS or rGO-GNS substrate. Moreover, the uniformity and reproducibility of the GNS@rGO-GNS substrate were studied by using thiophenol (TP) as a model analyte, which indicates that the SERS sensor exhibited superior signal reproducibility with an RSD value 5% and long-term stability with a minimal signal loss after 30 days. To demonstrate a potential application of our SERS substrate, SERS detection of the pesticide thiram in river water was realized with a limit of detection (LOD) up to 50 pM, showing the potential for new opportunities for efficient chemical and biological sensing applications.

The impact of pulsing a radio frequency (RF) glow discharge lamp on analytical aspects in glow discharge optical emission spectrometry (GD-OES) was investigated. The experiments were done with a LECO GDS950 spectrometer. This instrument has a fixed pulse frequency of 320  Hz and adjustable pulse duty cycles (PDC) 100%-6.4%. In the first part, emission yields (EY) were studied by measuring coated steel samples in compositional depth profiling (CDP) mode. Variations in EY were measured by integrating the intensity of emission lines from several elements through the entire coatings at several PDC settings. The results show generally small EY variations. For improved accuracy, a set of correction constants is suggested. In the second part, the impact on signal-to-background (S/B), signal-to-noise-noise (S/N), and precision was investigated using "high current" pulsing. This means increased pulse power leaving the average power constant at the different PDC settings. The samples were a low alloy steel and a high purity iron blank (background) sample. The results showed significant increase of the S/B and S/N for four out of six spectral lines investigated at increasing pulse power, showing potential for improved detection limits (DL). Furthermore, there was a tendency towards improved precision with higher pulse power. Finally, the effect on depth resolution in CDP was investigated by running a ZnNi coated steel using "high current" pulsing. It was found that the depth resolution was unaffected up to 30% PDC.

In a previous paper, we proposed the use of a set of colored LEGO blocks as "standard" samples for the evaluation of fluorescence avoidance and mitigation schemes in Raman spectroscopy, as well as for use to evaluate the instruments' performance on dark samples. The purpose of this paper is to establish that this set of LEGO blocks does represent a good test case for fluorescence avoidance and mitigation when using handheld Raman spectrometers, and for the ability to record Raman spectra from dark samples. The performance of ten different instruments, operating using different exciting lines (785, 830/852, and 1064 nm), and different data processing schemes, are compared. The combination of a series of colored blocks (white, yellow, red, and blue), and successively darker tone blocks (white, gray, and black) do challenge these instruments, and shed light on the ways that their manufacturers have optimized these instruments in specific areas and for different purposes.

A new method based on near-infrared (NIR) hyperspectral imaging (HSI) has been developed for online polymer film thickness mapping. Traditional online methods, including X-ray, capacitance, and physical gauging (micrometers), can only determine film thickness for a point with each measurement. The NIR-HIS method allows the determination of film thickness for a line based on each image, thus enabling true real-time two-dimensional (2D) mapping of film thickness as the film translates in front of the instrument. A Specim NIR camera, 1000-2500 nm, 384 (spatial) × 288 (spatial) pixels, was used in this study for various low-density polyethylene (LDPE), and high-density polyethylene (HDPE) films. Sample thickness between μm to mm can be mapped based on the myriad NIR absorbance bands with various molar absorptivity. The 2310 nm NIR peak was found to be the most effective feature for determining film thickness over the range of polyethylene film studied in this project: 10∼100 μm. A good correlation was found between the 2310 nm absorbance and the incumbent X-ray thickness scanner results. Interference fringes were found to be a potential source of error for quantitative analysis of thin films, and a classical least squares (CLS) analysis was found to be effective in removing fringes. This method was implemented to map out film thickness in real-time in an industrial blown film process.

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.

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.

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.