- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Efficient Tm:LiYF4 Lasers at ～2.3 lm: Effect of Energy-Transfer Upconversion
摘要： The 3H4 → 3H5 transition of Thulium ions (Tm3+), which features laser emission at ~2.3 μm is studied in details. We revise the conditions for efficient laser operation using a rate- equation model accounting for the ground-state bleaching, cross- relaxation and energy-transfer upconversion (ETU). We show that ETU has a crucial role in reaching more than unity pump quantum efficiency (QE) for ~2.3 μm Tm lasers based on highly- doped crystals. A Ti:Sapphire pumped quasi-continuous-wave 3.5 at.% Tm:LiYF4 laser generated 0.73 W at 2306 nm with a record-high slope efficiency of 47.3% (versus the absorbed pump power, for double-pass pumping) featuring a QE of 1.27. Diode- pumping of this crystal yielded a peak output power of >2 W. The first 2.3 μm Tm waveguide laser is also reported based on Tm:LiYF4 epitaxial layers with even higher doping of 6.2 at.% generating 0.23 W with a slope efficiency of 19.8%. The spectroscopic properties of Tm:LiYF4 relevant for the ~2.3 μm laser operation are revised as well.
关键词： mid-infrared,spectroscopy,laser transitions,Solid-state lasers
更新于2025-09-19 17:13:59
-
[IEEE 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - Berlin, Germany (2019.7.23-2019.7.27)] 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Dynamic Activation Patterns of the Motor Brain Revealed by Diffuse Optical Tomography <sup>*</sup>
摘要： Diffuse optical tomography (DOT), a subset of functional near-infrared spectroscopy (fNIRS), is a noninvasive functional imaging modality for studying the human brain in normal and diseased conditions. It measures changes in concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) in the blood vasculature of the brain. In contrast to functional magnetic resonance imaging (fMRI), the gold standard in human brain imaging, DOT offers the advantage of higher temporal resolution, portability, lower cost, multiple contrasts and usability for persons who cannot otherwise utilize MRI-based imaging modalities, including bedridden patients and infants, etc. The goal of the present study was to evaluate performance of a DOT method in studying dynamic patterns of brain activations involving motor control. CW-fNIRS data were acquired in four sessions from a healthy male participant when he performed a motor task in a block-design experiment. Results from experimental data showed pronounced activity in the primary motor cortex (M1), contralateral to the clenching hand. It was further observed that the M1 activity was consistent over four sessions. Furthermore, temporal dynamics of motor activity at each session further revealed well-sequenced activation patterns among M1, premotor cortex (PMC), and supplementary motor area (SMA). Timed ipsilateral motor activity suppression was also observed several seconds after the onset of contralateral M1 activity. More importantly, these temporal dynamics were similarly observed in all four sessions. These preliminary results suggest that the DOT method has the sensitivity, reliability, and spatio-temporal resolutions to study activities originated from the motor cortices. A full-scope evaluation and validation in more participants on the motor system can establish it as a promising neuroimaging tool to study, such as, infants at the risk of cerebral palsy or elders with Parkinson’s due to its portability and usability in clinical environments.
关键词： functional near-infrared spectroscopy,motor control,Diffuse optical tomography,brain activations,neuroimaging
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Chengdu, China (2019.5.21-2019.5.24)] 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) - Designs and Applications for the Controller Parameters of the Photovoltaic System
摘要： Many economically important minerals have absorption features in the short-wave infrared (SWIR; 2000–2500 nm). Sensors which measure this part of the spectrum cannot detect the wavelength minimum of a feature at ～900 nm (F900), indicative of ferric iron mineralogy. A method based on Gaussian processes (GPs) was developed and compared with multiple linear regression (MLR) to estimate the wavelength position of F900 from SWIR data (1002–1355 nm). SWIR data with different signal-to-noise ratios were acquired from crushed rock samples by a nonimaging spectrometer and an imaging spectrometer. GP estimates of wavelength position were converted to the proportion of goethite using coefficients from a regression of the proportion of goethite determined from X-ray diffraction (XRD) on wavelength position measured directly from spectra. GP-estimated wavelength positions were within the 2-nm and ～4-nm root-mean-square error of measurements made directly from spectra for nonimaging and imaging spectrometer data, respectively. Proportions of goethite derived from these estimates were respectively within 4% and 6% of the values measured by XRD. MLR performed poorly compared to GPs when applied to data with no added noise and failed when applied to data with added noise or to imaging spectrometer data. These findings indicate that the wavelength position of F900—an indicator of ferric iron mineralogy—can be estimated from data acquired at SWIR wavelengths (1002–1355 nm). This opens up possibilities for using a single (SWIR) sensor to acquire information on ferric iron mineralogy (using F900) and other minerals with diagnostic absorptions between 1000 and 2500 nm.
关键词： geology,infrared spectroscopy,iron,image sensors,remote sensing,Gaussian processes (GPs),mining industry,Electromagnetic radiation,spectral analysis,signal processing
更新于2025-09-19 17:13:59
-
Infrared spectroscopy coupled to cloud-based data management as a tool to diagnose malaria: a pilot study in a malaria-endemic country
摘要： Background: Widespread elimination of malaria requires an ultra?sensitive detection method that can detect low parasitaemia levels seen in asymptomatic carriers who act as reservoirs for further transmission of the disease, but is inexpensive and easy to deploy in the field in low income settings. It was hypothesized that a new method of malaria detection based on infrared spectroscopy, shown in the laboratory to have similar sensitivity to PCR based detection, could prove effective in detecting malaria in a field setting using cheap portable units with data management systems allowing them to be used by users inexpert in spectroscopy. This study was designed to determine whether the methodology developed in the laboratory could be translated to the field to diagnose the presence of Plasmodium in the blood of patients presenting at hospital with symptoms of malaria, as a precursor to trials testing the sensitivity of to detect asymptomatic carriers. Methods: The field study tested 318 patients presenting with suspected malaria at four regional clinics in Thailand. Two portable infrared spectrometers were employed, operated from a laptop computer or a mobile telephone with in?built software that guided the user through the simple measurement steps. Diagnostic modelling and validation testing using linear and machine learning approaches was performed against the gold standard qPCR. Sample spectra from 318 patients were used for building calibration models (112 positive and 110 negative samples according to PCR testing) and independent validation testing (39 positive and 57 negatives samples by PCR). Results: The machine learning classification (support vector machines; SVM) performed with 92% sensitivity (3 false negatives) and 97% specificity (2 false positives). The Area Under the Receiver Operation Curve (AUROC) for the SVM classification was 0.98. These results may be better than as stated as one of the spectroscopy false positives was infected by a Plasmodium species other than Plasmodium falciparum or Plasmodium vivax, not detected by the PCR primers employed. Conclusions: In conclusion, it was demonstrated that ATR?FTIR spectroscopy could be used as an efficient and reliable malaria diagnostic tool and has the potential to be developed for use at point of care under tropical field conditions with spectra able to be analysed via a Cloud?based system, and the diagnostic results returned to the user’s mobile telephone or computer. The combination of accessibility to mass screening, high sensitivity and selectivity, low logistics requirements and portability, makes this new approach a potentially outstanding tool in the context of malaria elimination programmes. The next step in the experimental programme now underway is to reduce the sample requirements to fingerprick volumes.
关键词： Plasmodium,Cloud based diagnostics,Infrared spectroscopy,Malaria diagnosis
更新于2025-09-19 17:13:59
-
Analysis of infrared spectra with narrow band absorption by a graphene/square-ring structure
摘要： A graphene covered on square-ring structure is designed and fabricated to achieve narrow band absorption of three peaks in the infrared band. The absorption rates of graphene/square-ring structures calculated by simulation are 90.49%, 65.67% and 20.38%, respectively, and the experimentally measured absorption rates are 82.12 %, 53.13 %, and 16.58 %, respectively. Comparing the absorption rate of simulation calculation with experimental measurement, as well as the reasons for the differences are presented. The dynamic control characteristics of the graphene device are not observed with this structure in the experiment, which is different from the simulation. We analyzed the reason for this distinction and proposed three solutions based on the experimental design. The research results of this paper provide an important reference to the design and preparation of graphene devices.
关键词： infrared spectroscopy,absorption,graphene,CVD
更新于2025-09-19 17:13:59
-
Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies
摘要： Knowledge of the seed vigor status of individual wheat kernels could provide scientific evidence for the screening of excellent germplasm and the breeding of seedlings. Although many factors collaborate to reduce or render seed vigor, many methods have been employed to detect individual kernel vigor. This study aims to demonstrate the feasibility for using near-infrared (NIR) spectroscopy to detect individual wheat seed vigor and determine suitable machine learning classification models. For this study, 1152 wheat kernel samples were selected, and five-sixths of the portion was treated by artificial aging (AA). All seeds spectra were acquired using a single-seed near-infrared system covering the spectral range of 1200–2400 nm. After NIR spectral collection, all kernels underwent a germination test to confirm their vigor. The spectral data from kernels within 3 germination days, 5 germination days and the non-germination kernels were further used for the development of three-category classification models. After pretreatment by using Savitzky-Golay (SG) second derivative-method and standard normal variate (SNV) correction, the high-dimension spectral data were smoothed, and then were reduced to select most effective wavelengths by two spectral dimensional reduction algorithms: principal component analysis (PCA) and successive projections algorithm (SPA). Four machine learning methodologies, support vector machine (SVM), extreme learning machine (ELM), random forest (RF) and adaptive boosting (AdaBoost) were combined with the two spectral dimensional reduction algorithms to build eight models to discriminate and predict each wheat kernel’s vigor. The results demonstrated that the eight three-category machine learning classification models developed with the two spectral dimensional reduction algorithms provided comparable results for individual wheat kernel vigor. The accuracies of the eight models were higher than 84.0%, and PCA-ELM and SPA-RF models afforded the two highest classification accuracies at 88.9% and 88.5%, respectively. The macro-average F1 of these two models were at the same level of 0.887, which means these two models had almost the same ability to assess kernel’s vigor. This study could serve as a major step towards the development of a fast and non-destructive high-throughput NIR-based sorting system of individual wheat kernel vigor determination for plant breeders, wheat quality inspectors, wheat processors, etc.
关键词： Multiple classification,Machine learning,Near-infrared spectroscopy,Multivariate data analysis,Wheat seed vigor
更新于2025-09-19 17:13:59
-
Characterizing Lewis Pairs Using Titration Coupled with In Situ Infrared Spectroscopy
摘要： Lewis acid-activation of carbonyl-containing substrates is a fundamental basis for facilitating transformations in organic chemistry. Historically, characterization of these interactions has been limited to models equivalent to stoichiometric reactions. Here, we report a method utilizing in situ infrared spectroscopy to probe the solution interactions between Lewis acids and carbonyls under synthetically relevant conditions. Using this method, we were able to identify 1:1 complexation between GaCl3 and acetone and a highly ligated complex for FeCl3 and acetone. The impact of this technique on mechanistic understanding is illustrated by application to the mechanism of Lewis acid-mediated carbonyl-olefin metathesis in which we were able to observe competitive binding interactions between substrate carbonyl and product carbonyl with the catalyst.
关键词： Lewis base,carbonyl,Lewis acid,infrared spectroscopy,titration,Issue 156,Chemistry,chemistry
更新于2025-09-19 17:13:59
-
Dynamic Ligand Surface Chemistry of Excited PbS Quantum Dots
摘要： The ligand shell around colloidal quantum dots mediates the electron and energy transfer processes that underpin their use in optoelectronic and photocatalytic applications. Here, we show that the surface chemistry of carboxylate anchoring groups of oleate ligands passivating PbS quantum dots undergoes significant changes when the quantum dots are excited to their excitonic states. We directly probe the changes of surface chemistry using time-resolved mid-infrared spectroscopy that records the evolution of the vibrational frequencies of carboxylate groups following excitation of the electronic states. The data reveal a reduction of the Pb?O coordination of carboxylate anchoring groups to lead atoms at the quantum dot surfaces. The dynamic surface chemistry of the ligands may increase their surface mobility in the excited state and enhance the ability of molecular species to penetrate the ligand shell to undergo energy and charge transfer processes that depend sensitively on distance.
关键词： surface chemistry,PbS quantum dots,ligand shell,colloidal quantum dots,time-resolved mid-infrared spectroscopy
更新于2025-09-19 17:13:59
-
Octane prediction from infrared spectroscopic data
摘要： A model for the prediction of research octane number (RON) and motor octane number (MON) of hydrocarbon mixtures and gasoline-ethanol blends has been developed based on infrared spectroscopy data of pure components. Infrared spectra for 61 neat hydrocarbon species were used to generate spectra of 148 hydrocarbon blends by averaging the spectra of their pure components on a molar basis. The spectra of 38 FACE (Fuels for Advanced Combustion Engines) gasoline blends were calculated using PIONA (Paraffin, Isoparaffin, Olefin, Naphthene, and Aromatic) class averages of the pure components. The study sheds light on the significance of dimensional reduction of spectra and shows how it can be used to extract scores with linear correlations to the following important features: molecular weight, paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, ethanolic OH groups, and branching index. Both scores and features can be used as input to predict octane numbers through nonlinear regression. Artificial Neural Network (ANN) was found to be the optimal method where the mean absolute error on a randomly selected test set was within the experimental uncertainty of RON, MON, and octane sensitivity.
关键词： octane prediction,infrared spectroscopy,hydrocarbon blends,artificial neural network,gasoline-ethanol blends,dimensional reduction
更新于2025-09-19 17:13:59
-
Detection of chalk in single kernels of long‐grain milled rice using imaging and visible/near‐infrared instruments
摘要： Background and objectives: To maintain the competitiveness of U.S. long‐grain rice in U.S. and foreign markets, having translucent whole milled grain is critical. An objective technique to detect grain chalk, opaque areas in the grain, will provide breeders and industry with an effective tool for developing low‐chalk varieties or agronomic practices that reduce chalk occurrence. Two instruments developed at the Center for Grain and Animal Health Research, U.S. Department of Agriculture‐Agricultural Research Service (USDA‐ARS), a single‐kernel near‐infrared (SKNIR) tube instrument and a silicon‐based light‐emitting diode (SiLED) high‐speed sorter, were compared with two commercially available imaging instruments, WinSEEDLE and SeedCount used for chalk quantification. Three 2‐way chalk classifications were defined for single kernels based on visual inspection: (a) <50% or ≥50% opacity or chalk (modified Grain Inspection, Packers & Stockyards Administration [GIPSA]), (b) <10% or ≥10% opacity (10% cutoff), and (c) 100% opacity or 100% translucent (MaxLevel). Findings: The SKNIR method provided the best classification for the modified GIPSA definition with an 82.4% average correct classification (CC), that is, 89% and 76% for nonchalky and chalky kernels, respectively. The WinSEEDLE had the best classification for the 10% cutoff definition, with an 84% CC for nonchalky kernels and a 96% CC for chalky kernels. For the MaxLevel definition, average CCs of both the SKNIR and SiLED methods were similar, at 93% and 95%, respectively. The average CCs were lower for both the WinSEEDLE method and the SeedCount method at 14% and 58%, respectively. These low CC values are a result of using a threshold of 100% for chalky or nonchalky kernels, where a single misclassified pixel within the image will cause misclassification. Calibration models developed for both the SKNIR and SiLED methods indicate that their classifications were based mainly on spectral differences near the adsorption bands for starch, protein, and water content. Conclusions: All of the instruments can be used to classify chalk, but their level of accuracy depends on how chalk is defined.
关键词： near‐infrared‐spectroscopy,rice chalk,imaging
更新于2025-09-19 17:13:59