Applied Spectroscopy最新文献

{"title":"Model Fitting and Analysis of Dielectric Properties in Alcohol-Fuel Blends Using Terahertz and Gigahertz Spectroscopies.","authors":"Alexis Cova-Bonillo, Rayda Patiño-Camino, George Brinklow, Magín Lapuerta, José Rodríguez-Fernández, Jorge H Melillo, Silvina Cerveny","doi":"10.1177/00037028241298300","DOIUrl":"10.1177/00037028241298300","url":null,"abstract":"<p><p>Alcohols from biological waste sources or renewable electricity (electrofuels) are gaining attention in hard-to-decarbonize sectors such as transport. Adding alcohol to conventional fuels has positive environmental effects on automotive applications, requiring minimal engine adjustments. Employing a combination of terahertz (THz) and gigahertz (GHz) spectroscopies, a comprehensive analysis of model fitting is presented for diesel-like fuels, pure alcohols (ethanol and n-butanol), and alcohol-fuel blends. Through the integration of data from both spectroscopic techniques, new Debye parameters are introduced to improve the accuracy of fitting for various fuels. This research demonstrates that THz spectroscopy alone is valuable for reasonable fits, particularly for alcohols. However, integrating THz and GHz spectroscopies leads to improved fitting, and to better potential to understand the behavior of fuel properties. In addition, the effect of alcohol concentration on the dielectric constant spectra in blends was investigated, highlighting the importance of molecular interactions. The results reveal a linear relationship between fitted parameters and alcohol content in the blends. However, the study acknowledges limitations, including challenges in achieving satisfactory fits at low alcohol concentrations and the necessity for assumptions in the modeling process. These findings provide a basis for future research and advances in fuel property modeling.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1056-1068"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gold Nanorod Surface-Enhanced Raman Spectroscopy Substrate for l-DOPA Detection: Experimental and Theoretical Approaches.","authors":"Tatiana Ap de Oliveira, Cibely S Martin, Rafael J G Rubira, Anerise de Barros, Italo O Mazali, Luiz P Zidoi, Augusto Batagin-Neto, Carlos J L Constantino","doi":"10.1177/00037028251315208","DOIUrl":"10.1177/00037028251315208","url":null,"abstract":"<p><p>Experimental efforts aimed at detecting levodopa (l-DOPA) using surface-enhanced Raman scattering (SERS) face a persistent challenge in obtaining a SERS signal with negatively charged nanoparticles. This challenge stems from the repulsion between deprotonated l-DOPA in aqueous solution and the charged surface of the nanoparticles, revealing dependencies on time and concentration to achieve the SERS signal. This study explores the adsorption mechanism of l-DOPA on the surface of gold nanorods (AuNRs) covered with a cetrimonium bromide (CTAB) bilayer as a colloidal solution, subsequently dried onto a solid substrate such as glass, silicon, and Au substrate. Experimental findings are supported by density functional theory theoretical calculations. The comparison between experimental and theoretical results highlights that the SERS profile can be attributed to the adsorption of l-DOPA via the catechol ring, leading to the formation of anionic and dianionic species.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1091-1101"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Techniques for Geochemical Analysis Using Laser-Induced Breakdown Spectroscopy.","authors":"Shamaila Akbar, M Inzmam Razzaq, Nasar Ahmed, Kamran Abbas, M Rafique, M Aslam Baig, Rinda Hedwig, Zahid Farooq","doi":"10.1177/00037028251334151","DOIUrl":"10.1177/00037028251334151","url":null,"abstract":"<p><p>In the present work, appropriate machine learning techniques coupled with LIBS have been proposed for the effective classification of multielement rock samples. To obtain the best classification efficiency most suitable emission lines were selected. Plasma on the surface of seventeen rock samples was generated using a 532  nm Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser, and optical emission spectra were collected via an Avantes spectrometer. Well-isolated signature emission lines corresponding to detected elements (Ca, Mg, Na, K, Fe, Ba, Sr, Si, Al, and Li) were chosen as input for the machine learning algorithms. Three machine learning techniques, including analysis of variance (ANOVA), principal component analysis (PCA), and PCA coupled with standard normal variate (SVM), were utilized on normalized intensities of selected spectral lines of detected elements. ANOVA testing on the selected lines was employed to assess the normality and suitability of data for further machine learning techniques. The combination of laser-induced breakdown spectroscopy (LIBS) with PCA enabled a comprehensive classification of rock samples. The linearity and efficiency of PCA were enhanced by utilizing the support vector machine (SVM), resulting in the accurate classification of rock samples. This study demonstrates that to assess the effective classification of multielement rock samples the appropriate emission lines and machine learning techniques are crucial. Using this methodology results become more reliable as compared to conventional machine learning techniques.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1129-1141"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Use of Fourier Transform Raman Spectroscopy for Pollen Chemical Characterization.","authors":"Florian Muthreich, Valeria Tafintseva, Boris Zimmermann, Achim Kohler, Carlos M Vila-Viçosa, Alistair W R Seddon","doi":"10.1177/00037028251334405","DOIUrl":"10.1177/00037028251334405","url":null,"abstract":"<p><p>Vibrational spectroscopy is gaining popularity for understanding ecological and evolutionary patterns in plants, particularly in relation to the analysis of pollen grains. So far, Fourier transform infrared spectroscopy (FT-IR) has been the main approach used to classify pollen grains based on chemical variations. However, FT-IR may be less suitable for detecting differences in the pollen grain exine, mainly composed of sporopollenin. In contrast, Raman spectroscopy has increased sensitivity for the main chemical components found within sporopollenins. We compare the classification performance and chemical information provided by FT-IR and FT-Raman using a large dataset of <i>Quercus</i> L. pollen, comprising five species in three sections: (i) <i>Cerris</i>: <i>Q</i>. <i>suber</i>, (ii) <i>Ilex</i>: <i>Q. coccifera</i>, <i>Q. rotundifolia</i>, and (iii) <i>Quercus</i>: <i>Q. robur</i>, <i>Q. faginea</i>). Here, we used multiblock sparse partial least squares discriminant analyses (MB-sPLS-DA) analyses to directly compare the two infrared methods. Both FT-IR and FT-Raman successfully classified <i>Quercus</i> pollen to section level (100% accuracy). At the species level our models achieved ∼90% accuracy for FT-Raman and FT-IR separately and in the combined multiblock model. The multiblock results showed an increased number of sporopollenin peaks observed in FT-Raman spectra as compared to FT-IR. These peaks are also of a higher importance for classification. Results also showed differences in the types of vibrations that are of diagnostic value for the two infrared methods. CH₂ deformations are more important in FT-Raman, while C-O-C, C-O, and C = O stretches are more important for FT-IR-based identification of pollen. These vibrations are indicators of carbohydrates, proteins and lipids. FT-Raman provides equally successful diagnostic potential to FT-IR, but uses more chemical information based on variations in sporopollenin chemistry than FT-IR. We suggest that the combined analysis of FT-IR and FT-Raman using multiblock analysis has great potential for classification.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1142-1154"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Continuous Monitoring of Sulfur Dioxide Removal Using K-Band Molecular Rotational Resonance Spectroscopy.","authors":"Sai Eswar Jasti, Md Abrar Jamil, Chandru P Chandrasekaran, Suying Wei, Sylvestre Twagirayezu","doi":"10.1177/00037028251317215","DOIUrl":"10.1177/00037028251317215","url":null,"abstract":"<p><p>Sulfur dioxide (SO₂), an air pollutant, poses significant threats to both public health and the environment. It is one of the six air pollutants regulated by the U.S. Environmental Protection Agency (EPA) under the Clean Air Act. In efforts to determine the application of molecular rotational resonance (MRR) spectroscopy for monitoring SO₂ and its removal from point sources, a K-band MRR technique was evaluated. This method was applied to measure the products of heated mixtures of SO₂ and oxygen (O₂) in the presence of ammonium metavanadate (NH₄VO₃) as a catalyst. The observed MRR spectrum revealed the presence of SO₂, water vapor (H₂O), and ammonia (NH₃) due to the sensitivity of MRR to only polar species. SO₂ removal was further confirmed by the disappearance of SO₂ as NH₃ formed. The work presented here analyzed the measurements of SO₂ and validated K-band MRR for monitoring SO₂ removal. It was observed that the K-band MRR maintains its linearity and other polar species in the mixture did not interfere with MRR signature of SO₂. The limit of detection, better than 1%, was determined by evaluating targeted K-band MRR signal response of SO₂ removal obtained at varying partial pressures of SO₂ in the mixture and using the MRR signal of pure SO₂ at 3 mTorr as a reference (100%). Additionally, the results showed that the accuracy and precision of K-band MRR for measuring SO₂ partial pressure were satisfactory.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1155-1163"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143381481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advertising and Front Matter.","authors":"","doi":"10.1177/00037028251355335","DOIUrl":"https://doi.org/10.1177/00037028251355335","url":null,"abstract":"","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":"79 7","pages":"1031-1034"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of 6-Thioguanine Functionalized Molybdenum-Copper Bimetallic Nanoclusters with Fluorescence Spectroscopy for the Sensitive Detection of Uric Acid in Biofluids.","authors":"Harshita, Tae Jung Park, Suresh Kumar Kailasa","doi":"10.1177/00037028241292056","DOIUrl":"10.1177/00037028241292056","url":null,"abstract":"<p><p>In this paper, a single-step synthetic approach is presented for the development of bimetallic molybdenum-copper nanoclusters (Mo-CuNCs), shielded by a small molecule 6-thioguanine (6-TG). The Mo-CuNCs possessed a small size, high fluorescence, stable behavior, and good solubility in water. The 6-TG-Mo-CuNCs exhibit strong blue fluorescence emission at 410 nm after exciting at 330 nm as compared to its monometallic nanoclusters. Utilizing 6-TG-Mo-CuNCs superior biochemical stability, uric acid (UA) can be specifically detected as an oxidative stress biomarker using an inner filter effect mechanism. The probe demonstrated good sensing capability for detecting UA within the range of 0.09-5.00 μM and a detection limit of 0.237 μM. The method feasibility is further validated by quantifying UA in urine and plasma samples.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1069-1077"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142613926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral Data Fusion from Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident.","authors":"Janos I Braun, Paige E Anderson, Justin I Borrero Negrón, Kyle C Hartig, Ashwin P Rao","doi":"10.1177/00037028241298305","DOIUrl":"10.1177/00037028241298305","url":null,"abstract":"<p><p>This work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO<math><msub><mrow></mrow><mn>2</mn></msub></math>) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. Lastly, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10^-6 and calculated limit of detection order 10^-5 wt<math><mi>%</mi></math>.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1078-1090"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principal Component Analysis and Near-Infrared Spectroscopy as Noninvasive Blood Glucose Assay Methods.","authors":"Hadi Barati, Arian Mousavi Madani, Sorena Shadzinavaz, Mehdi Fardmanesh","doi":"10.1177/00037028241300535","DOIUrl":"10.1177/00037028241300535","url":null,"abstract":"<p><p>In this paper, a new model is presented for estimation of the blood glucose level from the measured near-infrared absorbance. The model has been developed in such a way that the regression coefficients of this linear relation have been approximated by considering only the molar absorptivity of the glucose and the obtained coefficients have been utilized to estimate the blood glucose levels from the measured absorbances. The estimation of the blood glucose concentrations by this blind approach exhibited an acceptable accuracy in comparison to the more accurate principal components regression method. The blood sample absorbances have been measured using a Fourier transform infrared device while the blood glucose levels have been determined by a commercial finger-prick glucometer device.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1047-1055"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Cavity Ring-Down Spectroscopy Performance and Procedures for High-Throughput <ns0:math><ns0:msup><ns0:mi>δ</ns0:mi><ns0:mn>18</ns0:mn></ns0:msup></ns0:math>O and <ns0:math><ns0:msup><ns0:mi>δ</ns0:mi><ns0:mn>2</ns0:mn></ns0:msup></ns0:math>H Measurement in Water Using \"Express\" Mode.","authors":"Nir Galili, Thomas M Blattmann, Anna Somlyay, Nora Gallarotti, Timothy I Eglinton, Jordon D Hemingway","doi":"10.1177/00037028241302355","DOIUrl":"10.1177/00037028241302355","url":null,"abstract":"<p><p>Cavity ring-down spectroscopy (CRDS) is rapidly becoming an invaluable tool to measure hydrogen (δ²H) and oxygen (δ¹⁸O) isotopic compositions in water, yet the long-term accuracy and precision of this technique remain relatively underreported. Here, we critically evaluate one-year performance of CRDS δ²H and δ¹⁸O measurements at ETH Zurich, focusing on high throughput (~200 samples per week) while maintaining required precision and accuracy for diverse scientific investigations. We detail a comprehensive methodological and calibration strategy to optimize CRDS reliability for continuous, high-throughput analysis using Picarro's \"Express\" mode, an area not extensively explored previously. Using this strategy, we demonstrate that CRDS achieves long-term precision better than ±0.5‰ for δ¹⁸O and ±1.0‰ for δ²H (±1σ) on three United States Geological Survey (USGS) reference materials treated as unknowns.¹⁸ Specifically, reported results for each reference material over this one-year period are: (i) USGS W-67444: <math><msup><mi>δ</mi><mn>2</mn></msup></math>H = <math><mrow><mo>-</mo><mn>399.32</mn><mo>±</mo><mn>0.96</mn></mrow><mtext>‰</mtext></math>, <math><msup><mi>δ</mi><mn>18</mn></msup></math>O = <math><mrow><mo>-</mo><mn>51.07</mn><mo>±</mo><mn>0.45</mn></mrow><mtext>‰</mtext></math> (<math><mi>n</mi><mo>=</mo><mn>30</mn></math>), (ii) USGS W-67400: <math><msup><mi>δ</mi><mn>2</mn></msup></math>H = <math><mrow><mn>2.55</mn><mo>±</mo><mn>0.49</mn></mrow><mtext>‰</mtext></math>, <math><msup><mi>δ</mi><mn>18</mn></msup></math>O = <math><mrow><mo>-</mo><mn>1.85</mn><mo>±</mo><mn>0.13</mn></mrow><mtext>‰</mtext></math> (<math><mi>n</mi><mo>=</mo><mn>140</mn></math>), and (iii) USGS-50: <math><msup><mi>δ</mi><mn>2</mn></msup></math>H = <math><mrow><mn>33.68</mn><mo>±</mo><mn>0.91</mn></mrow><mtext>‰</mtext></math>, <math><msup><mi>δ</mi><mn>18</mn></msup></math>O = <math><mrow><mn>5.03</mn><mo>±</mo><mn>0.04</mn></mrow><mtext>‰</mtext></math> (<math><mi>n</mi><mo>=</mo><mn>21</mn></math>). We also address challenges such as aligning our analytical uncertainties with the narrower uncertainties of International Atomic Energy Agency reference materials, and mitigating inherent CRDS issues like memory and matrix effects when analyzing environmental samples. Our review provides a practical framework for CRDS applications in hydrology, paleoclimatology, and biogeochemistry, underscoring the importance of continuous evaluation and methodological refinement to ensure accuracy and precision in δ²H and δ¹⁸O analyses.¹⁸.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1120-1128"},"PeriodicalIF":2.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}