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Results for “quantitative analysis”
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Direct method for the quantitative analysis of surface contamination on ultra-low background materials from exposure to dust
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Quantitative Analysis of the Morphology of {101} and {001} Faceted Anatase TiO 2 Nanocrystals and Its Implication on Photocatalytic Activity
The atomistic structure and morphology (shape and size) of nanomaterials have strong influences on their physical and chemical properties. However, many characterization techniques focus exclusively on one length-scale regime or another when developing quantitative morphology/structural models. In this article, we demonstrate that powder X-ray diffraction and neutron pair distribution function (PDF) can be used to obtain accurate average morphology and atomistic structure of {001} and {101} faceted anatase TiO 2 nanocrystals based on differential evolution refinements using Debye scattering equation calculations. It is also demonstrated that the morphology polydispersity of TiO 2 nanocrystals can be effectively obtained from the diffraction data via a numerical refinement routine. The morphology refinement results are in good agreement with those from transmission electron microscopy and the modeling of small angle neutron scattering data. This method is successfully used to quantify the facet-specified photocatalytic hydrogen evolution activity of anatase TiO 2 nanocrystals with different {001} to {101} ratios. It is found that the sample with an intermediate amount of both {001} and {101} facets shows the best photocatalytic hydrogen evolution reaction (HER) activity. It is expected that the simultaneous structure and morphology refinement technique can be generally used to study the relationship between morphology and functionality of nanomaterials.
Quantitative Analysis Guidance in Safety Software Validation
Quantitative analysis in software development has been a challenging topic over the years in safety software. While qualitative processes for vigorous safety software verification and validation (V and V) have been provided through standard such as IEEE 1012, quantitative analysis for V and V tasks such as correctness, completeness or accuracy evaluation have not been formally defined. In practice, there are quantitative analyses in terms of using software metrics. Many of these software metrics are useful in analyzing the test results. Some of them are used for providing guidance in code evaluation activities e.g. numbers of line of codes not exceeding certain limits, recursive calls are not allowed, etc. However, few can be used for providing quantitative analysis guidance for the V and V tasks such as completeness evaluation. AREVA Federal Services (AFS) has provided services for many nuclear projects in US. Through compliance with IEEE 1012, AFS V and V processes have provided vigorous qualitative processes ensuring quality level of the software products used in the project meeting the required nuclear software level. In addition, quantitative analysis is developed to provide structured validation framework ensuring 'zero defect' in the software products. This paper describes some aspects of the quantitative approaches using a sample safety application. (authors)
Raman Spectroscopy Coupled with Chemometric Analysis for Speciation and Quantitative Analysis of Aqueous Phosphoric Acid Systems
Complex chemical systems that exhibit varied and matrix-dependent speciation are notoriously difficult to monitor and characterize on-line and in real-time. Optical spectroscopy is an ideal tool for in situ characterization of chemical species that can enable quantification as well as species identification. Chemometric modeling, a multivariate method, has been successfully paired with optical spectroscopy to enable measurement of analyte concentrations even in complex solutions where univariate methods such as Beer’s law analysis fail. Here, Raman spectroscopy is used to quantify the concentration of phosphoric acid and its three deprotonated forms during a titration. In this system, univariate approaches would be difficult to apply due to multiple species being present simultaneously within the solution as pH is varied. Locally Weighted Regression (LWR) modeling was used to determine phosphate concentration from spectral signature. LWR results, in tandem with Multivariate Curve Resolution modeling, provide direct measurement of the concentration of each phosphate species using only the Raman signal. Furthermore, results are presented within the context of fundamental solution chemistry, including Pitzer equations to compensate for activity coefficients and non-idealities associated with high ionic strength systems.
Assessment of acute myocarditis by cardiac magnetic resonance imaging: Comparison of qualitative and quantitative analysis methods
Background: To compare cardiac magnetic resonance (CMR) qualitative and quantitative analysis methods for the noninvasive assessment of myocardial inflammation in patients with suspected acute myocarditis (AM). Methods: A total of 61 patients with suspected AM underwent coronary angiography and CMR. Qualitative analysis was performed applying Lake-Louise Criteria (LLC), followed by quantitative analysis based on the evaluation of edema ratio (ER) and global relative enhancement (RE). Diagnostic performance was assessed for each method by measuring the area under the curves (AUC) of the receiver operating characteristic analyses. The final diagnosis of AM was based on symptoms and signs suggestive of cardiac disease, evidence of myocardial injury as defined by electrocardiogram changes, elevated troponin I, exclusion of coronary artery disease by coronary angiography, and clinical and echocardiographic follow-up at 3 months after admission to the chest pain unit. Results: In all patients, coronary angiography did not show significant coronary artery stenosis. Troponin I levels and creatine kinase were higher in patients with AM compared to those without (both P < .001). There were no significant differences among LLC, T2-weighted short inversion time inversion recovery (STIR) sequences, early (EGE), and late (LGE) gadolinium-enhancement sequences for diagnosis of AM. The AUC for qualitative (T2-weighted STIR 0.92, EGE 0.87 and LGE 0.88) and quantitative (ER 0.89 and global RE 0.80) analyses were also similar. Conclusions: Qualitative and quantitative CMR analysis methods show similar diagnostic accuracy for the diagnosis of AM. These findings suggest that a simplified approach using a shortened CMR protocol including only T2-weighted STIR sequences might be useful to rule out AM in patients with acute coronary syndrome and normal coronary angiography.
Calcium fluoride as a dominating matrix for quantitative analysis by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS): A feasibility study
Here, calcium fluoride formed by the reaction between ammonium bifluoride and calcium chloride was investigated as a dominating matrix for quantitative analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Transformation from a solid sample to the calcium fluoride-based matrix permitted quantitative analysis based on calibration standards made from elemental standards. A low abundance stable calcium isotope, i.e. 44 Ca + , was monitored as the internal standard for quantitative analysis by LA-ICP-MS. Correlation coefficient factors for multiple elements were obtained with values over 0.999. The results for multiple elements in a certified reference material of soil (NIST SRM 2710a) agreed with the certified values in the range of expanded uncertainty, indicating the present method was valid for quantitation of elements in solid samples.
Early Assessment of Treatment Responses During Radiation Therapy for Lung Cancer Using Quantitative Analysis of Daily Computed Tomography
Purpose: To investigate early tumor and normal tissue responses during the course of radiation therapy (RT) for lung cancer using quantitative analysis of daily computed tomography (CT) scans. Methods and Materials: Daily diagnostic-quality CT scans acquired using CT-on-rails during CT-guided RT for 20 lung cancer patients were quantitatively analyzed. On each daily CT set, the contours of the gross tumor volume (GTV) and lungs were generated and the radiation dose delivered was reconstructed. The changes in CT image intensity (Hounsfield unit [HU]) features in the GTV and the multiple normal lung tissue shells around the GTV were extracted from the daily CT scans. The associations between the changes in the mean HUs, GTV, accumulated dose during RT delivery, and patient survival rate were analyzed. Results: During the RT course, radiation can induce substantial changes in the HU histogram features on the daily CT scans, with reductions in the GTV mean HUs (dH) observed in the range of 11 to 48 HU (median 30). The dH is statistically related to the accumulated GTV dose (R{sup 2} > 0.99) and correlates weakly with the change in GTV (R{sup 2} = 0.3481). Statistically significant increases in patient survival rates (P=.038) were observed for patients with a higher dH in the GTV. In the normal lung, the 4 regions proximal to the GTV showed statistically significant (P<.001) HU reductions from the first to last fraction. Conclusion: Quantitative analysis of the daily CT scans indicated that the mean HUs in lung tumor and surrounding normal tissue were reduced during RT delivery. This reduction was observed in the early phase of the treatment, is patient specific, and correlated with the delivered dose. A larger HU reduction in the GTV correlated significantly with greater patient survival. The changes in daily CT features, such as the mean HU, can be used for early assessment of the radiation response during RT delivery for lung cancer.
Quantitative Analysis of Ca, Mg, and K in the Roots of Angelica pubescens f. biserrata by Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Networks
Laser-induced breakdown spectroscopy has been applied for the quantitative analysis of Ca, Mg, and K in the roots of Angelica pubescens Maxim. f. biserrata Shan et Yuan used in traditional Chinese medicine. Ca II 317.993 nm, Mg I 517.268 nm, and K I 769.896 nm spectral lines have been chosen to set up calibration models for the analysis using the external standard and artificial neural network methods. The linear correlation coefficients of the predicted concentrations versus the standard concentrations of six samples determined by the artificial neural network method are 0.9896, 0.9945, and 0.9911 for Ca, Mg, and K, respectively, which are better than for the external standard method. The artificial neural network method also gives better performance comparing with the external standard method for the average and maximum relative errors, average relative standard deviations, and most maximum relative standard deviations of the predicted concentrations of Ca, Mg, and K in the six samples. Finally, it is proved that the artificial neural network method gives better performance compared to the external standard method for the quantitative analysis of Ca, Mg, and K in the roots of Angelica pubescens.
Quantitative analysis of changes in antioxidant in crosslinked polyethylene (XLPE) cable insulation material exposed to heat and gamma radiation
Quantitative analysis of the antioxidant poly(1,2-dihydro-2,2,4-trimethylquinoline) (pTMQ) was conducted on pristine, thermally-aged, and gamma radiation-aged commercial cross-linked polyethylene-(XLPE-)based cable insulation material aged at temperatures 60, 90, and 115?°C, with gamma radiation exposure dose rates of 0, 120, 300, and 540?Gy/h for 15 days. The quantification of antioxidant was performed using pyrolysis gas chromatography-mass spectrometry (Py-GCMS). Oxidation induction time (OIT) was measured using differential scanning calorimetry (DSC) and correlation was made between the quantified depletion of antioxidant and measured OIT. It was observed that, in the case of isothermal aging, the quantity of antioxidant and OIT decreased with increasing gamma radiation dose. In the case of samples exposed to the same gamma radiation dose, the quantity of antioxidant and OIT were observed to decrease with increasing aging temperature. Depletion in the quantity of antioxidant relative to that in the pristine material ranged from 7 to 93% for differently aged samples. The measured decline in OIT ranged from 0 to 80%. Change in the quantity of antioxidant in the material was observed to follow the same trend as the change in OIT when the samples were aged under various conditions. The observations are explained in terms of the reaction between the antioxidant and free radicals created during exposure of the samples to thermal and gamma radiation.
Quantitative analysis method for multi-color microscopy
We developed an analysis framework for quantitatively interpreting results from multi-color fluorescence microscopy data. When the emission signal from fluorescent probes (i.e. from biological microscopy or in single-particle microscopy) is spectrally separated before detection, the resulting signals contain information about the strengths of each probe. We built a model to deconvolve the raw data into quantitative descriptions of either of the wavelength of a single probe or the relative strengths among multiple probes. This approach requires only spectrally separating the image data into two or more channels, as opposed to fully resolving the spectral profile, thus allowing other microscopy techniques to be used simultaneously. Our analysis framework incorporates the response features of the optical and imaging elements used to produce the spectral separation, yielding highly quantitative interpretations of the underlying behaviors that give rise to the data.
Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel
Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.
New Cluster Secondary Ions for Quantitative Analysis of the Concentration of Boron Atoms in Diamond by Time-of-Flight Secondary-Ion Mass Spectrometry
A new approach to quantitative analysis of the concentration of boron atoms in diamond using secondary- ion mass spectrometers with time-of-flight mass analyzers is proposed. Along with the known boron-containing lines (B, BC, BC{sub 2}), many lines related to cluster secondary ions BC{sub N} have been found in the mass spectrum; their intensity increases by one or two orders of magnitude when Bi{sub 3} probe ions are used. Lines BC{sub 4}, BC{sub 6}, BC{sub 2}, and BC{sub 8} have the highest intensity (in the descending order); when they are summed, the sensitivity increases by an order of magnitude in comparison with the known mode of detecting BC{sub 2}. The parameters of the boron δ-layer in single-crystal diamond films grown under optimal conditions have been measured to be unprecedented: the δ-layer width is about 2 nm, and the concentration is 6.4 × 10{sup 20} cm{sup –3} (the boron concentrations for doped and undoped diamonds differ by four orders of magnitude).
Hyperspectral Imaging and SPA–LDA Quantitative Analysis for Detection of Colon Cancer Tissue
Hyperspectral imaging (HSI) has been demonstrated to provide a rapid, precise, and noninvasive method for cancer detection. However, because HSI contains many data, quantitative analysis is often necessary to distill information useful for distinguishing cancerous from normal tissue. To demonstrate that HSI with our proposed algorithm can make this distinction, we built a Vis–NIR HSI setup and made many spectral images of colon tissues, and then used a successive projection algorithm (SPA) to analyze the hyperspectral image data of the tissues. This was used to build an identification model based on linear discrimination analysis (LDA) using the relative reflectance values of the effective wavelengths. Other tissues were used as a prediction set to verify the reliability of the identification model. The results suggest that Vis–NIR hyperspectral images, together with the spectroscopic classification method, provide a new approach for reliable and safe diagnosis of colon cancer and could lead to advances in cancer diagnosis generally.
Effect of excess lithium in LiMn2O4 and Li1.15Mn1.85O4 electrodes revealed by quantitative analysis of soft X-ray absorption spectroscopy
We performed a comparative study of the soft x-ray absorption spectroscopy of the LiMn2O4 and Li1.15Mn1.85O4 electrode materials with a quantitative analysis of the Mn oxidation states. The revealed redox evolution of Mn upon the electrochemical cycling clarifies the effect of the excess Li in the materials, which naturally explains the different electrochemical performance. The spectral analysis perfectly agrees with the different initial cycling capacities of the two materials. The results show unambiguously that Mn3+ starts to dominate the electrode surface after only one cycle. More importantly, the data show that, while LiMn2O4 electrodes follow the nominal Mn redox evolution, the formation of Mn3+ on the electrode surface is largely retarded for the Li1.15Mn1.85O4 during most of the electrochemical process. Such a different surface Mn redox behavior leads to differences in the detrimental effects of Mn2+ formation on the surface, which is observed directly after only two cycles. Our results provide strong evidence that a key effect of the (bulk) excess Li doping is actually due to processes on the electrode surfaces.
Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns
The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.
A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images
Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.