PDMS diaphragm based miniature fiber-optic Fabry-Perot dynamic pressure sensor for turbomachinery application.

Small-sized, highly sensitive dynamic pressure sensors are crucial in the field of turbomachinery application. In this paper, a fiber-tip structure dynamic pressure sensor utilizing a small piece of glass tube as the air cavity and PDMS material as the diaphragm was fabricated. It has the advantage of being small in size with the diameter of 125 µm while having high sensitivity of 26.26 pm/kPa. The fabrication process was described in detail, which is simple and cost-effective. The sensor characteristics were studied theoretically and experimentally. Quasi-square pressure signal of different frequencies generated by the siren disk were measured by the sensor and compared with that obtained from the commercial piezoresistive pressure sensor to verify the accuracy of the proposed sensor. The R2 of the four pairs of pressure waveforms were 0.94, 0.81, 0.93, and 0.96, respectively. Stability testing of the sensor was also performed, showing that the sensor can work reliably under dynamic pressure environment.

Image fusion guidance for left subclavian artery in situ fenestration during thoracic endovascular repair.

OBJECTIVES: To evaluate the feasibility and clinical benefit of utilizing image fusion for thoracic endovascular repair (TEVAR) with in situ fenestration (ISF-TEVAR). MATERIALS AND METHODS: Between January 2020 and December 2020, we prospectively collected 18 consecutive cases with complex thoracic aortic lesions who underwent image fusion guided ISF-TEVAR. As a control group, 18 patients were collected from historical medical records from June 2019 to December 2019. The fusion group involved the use of 3D fusion of CTA and fluoroscopic images for real-time 3D guidance, and the control group involved the use of only regular fluoroscopic images for guidance. The total contrast medium volume, hand-injected contrast medium volume, overall operative time, radiation dose and fluoroscopy time were compared between the two groups. Accuracy was measured based on preoperative CTA and intraoperative digital subtraction angiography. RESULTS: 3D fusion imaging guidance was successfully implemented in all patients in the fusion group. Hand-injected contrast medium volume and overall operative time were significantly lower in the fusion group than in the control group (p = .028 and p = .011). Compared with the control group, the fusion group showed a significant reduction in time and radiation dose-area product (DAP) for fluoroscopy (p = .004 and p = .010). No significant differences in total radiation dose (DAP) or total contrast medium volume were observed (p = .079 and p = .443). Full accuracy was achieved in 8 cases (44%), with a mean deviation of 2.61 mm ± 3.1 (range 0.0-8.4 mm). CONCLUSIONS: 3D image fusion for ISF-TEVAR was associated with a significant reduction in hand-injected contrast medium, time and radiation exposure for fluoroscopy and overall operative time. The image fusion guidance showed potential clinical benefits towards improved treatment safety and accuracy for complex thoracic endovascular interventions.

Enhanced stent visualization system for percutaneous coronary intervention in patients with chronic kidney disease: effects on contrast media volume and radiation exposure.

OBJECTIVE: We aimed to assess the impact of using enhanced stent visualization (ESV) systems on contrast media volume and radiation dose in percutaneous coronary intervention (PCI), especially for patients with chronic kidney disease (CKD). BACKGROUND: Coronary heart disease (CHD) is associated with chronic kidney disease (CKD), as they share a similar pathological pathway. In addition, the iodinated contrast media used for angiography is a risk factor for contrast-associated acute kidney injury (CA-AKI), which could aggravate the progression of CKD. We hypothesized that ESV systems have the potential to reduce the use of contrast media as well as the radiation dose; however, few studies have reported the impact on contrast media with the use of ESV systems. METHODS: We retrospectively collected 124 patients with acute coronary syndrome who underwent PCI from May 2020 to July 2021. The patients were divided into the ESV-guided group (n = 64) and angiography-guided group (n = 60). Procedural parameters, including contrast media volume, radiation exposure (in Air Kerma-AK and Dose Area Product-DAP), number of cines, cine frames, fluoroscopy and procedure time, were recorded and analysed. RESULTS: The groups were comparable regarding the patient characteristics. There was a significant reduction in contrast media volume (174.7 ± 29.6 ml vs.132.6 ± 22.3 ml, p = 0.0001), radiation exposure (776 (499 - 1200) mGy vs. 1065 (791 - 1603) mGy, p = 0.002 in AK; 43 (37 - 73) Gycm2 vs. 80 (64 - 133) Gycm2, p = 0.030 in DAP) and procedure time (53.06 ± 21.20 min vs. 72.00 ± 30.55 min, p = 0.01) with the use of ESV systems. Similar results were observed in the subgroup analysis for the patients with CKD. CONCLUSION: This study suggested that the use of ESV is associated with reduced contrast media usage, radiation dose and procedure time during PCI. The same results were observed in a subgroup analysis in patients with CKD, and this shows that ESV-guided PCI has the potential to reduce renal impairment and mitigate the progression of CKD for those CHD patients with CKD.

Research on the difference between patients with coronary heart disease and healthy controls by surface enhanced Raman spectroscopy.

Coronary heart disease (CHD) is one of the primary causes of death globally. There are several diagnostic techniques for CHD at present, but they are invasive and with limited accuracy. In the work, measurement of human urine based on surface-enhanced Raman spectroscopy (SERS) was proposed to diagnose CHD. Urine samples of 157 CHD patients and 63 healthy controls (HC) were investigated by SERS. Statistical analysis of the measured data was then performed. It was found that there were intensity differences in nine Raman peaks (1223/1243/1272/1463/1481/1516/1536/1541/1550 cm-1) between CHD and HC in their average SERS spectrum. Furthermore, principal component analysis (PCA)-linear discriminant analysis (LDA) was then utilized to establish a prediction model to classify CHD and HC. It revealed that the accuracy, specificity and sensitivity of the prediction model validated by leave-one-patient-out cross validation (LOPOCV) were 84.09%, 92.06% and 80.89%, respectively. Therefore, the proposed method can be employed as a non-invasive, rapid and accurate tool for CHD diagnosis in clinical application.

Demographics, in-hospital analysis, and prevalence of 33 rare diseases with effective treatment in Shanghai.

BACKGROUND: Rare diseases are ailments which impose a heavy burden on individual patients and global society as a whole. The rare disease management landscape is not a smooth one-a rare disease is quite often hard to diagnose, treat, and investigate. In China, the country's rapid economic rise and development has brought an increased focus on rare diseases. At present, there is a growing focus placed on the importance and public health priority of rare diseases and on improving awareness, definitions, and treatments. METHODS: In this work we utilized clinical data from the Shanghai HIE System to characterize the status of 33 rare diseases with effective treatment in Shanghai for the time period of 2013-2016. RESULTS AND CONCLUSION: First, we describe the total number of patients, year-to-year change in new patients with diagnosis in one of the target diseases and the distribution of gender and age for the top six (by patient number) diseases of the set of 33 rare diseases. Second, we describe the hospitalization burden in terms of in-hospital ratio, length of stay, and medical expenses during hospitalization. Finally, rare disease period prevalence is calculated for the rare diseases set.

Improving the Diagnosis of Phenylketonuria by Using a Machine Learning-Based Screening Model of Neonatal MRM Data.

Phenylketonuria (PKU) is a common genetic metabolic disorder that affects the infant's nerve development and manifests as abnormal behavior and developmental delay as the child grows. Currently, a triple-quadrupole mass spectrometer (TQ-MS) is a common high-accuracy clinical PKU screening method. However, there is high false-positive rate associated with this modality, and its reduction can provide a diagnostic and economic benefit to both pediatric patients and health providers. Machine learning methods have the advantage of utilizing high-dimensional and complex features, which can be obtained from the patient's metabolic patterns and interrogated for clinically relevant knowledge. In this study, using TQ-MS screening data of more than 600,000 patients collected at the Newborn Screening Center of Shanghai Children's Hospital, we derived a dataset containing 256 PKU-suspected cases. We then developed a machine learning logistic regression analysis model with the aim to minimize false-positive rates in the results of the initial PKU test. The model attained a 95-100% sensitivity, the specificity was improved 53.14%, and positive predictive value increased from 19.14 to 32.16%. Our study shows that machine learning models may be used as a pediatric diagnosis aid tool to reduce the number of suspected cases and to help eliminate patient recall. Our study can serve as a future reference for the selection and evaluation of computational screening methods.

Measurement of the mean aspect ratio and two characteristic dimensions of polydisperse arbitrary shaped nanoparticles, using translational-rotational ultrafast image-based dynamic light scattering.

In this paper, we propose a method to determine the mean value of two characteristic dimensions and the mean aspect ratio of a sample of polydisperse arbitrary shaped nanoparticles named translational-rotational ultrafast image-based dynamic light scattering (TR-UIDLS), which is extended from the ultrafast image-based dynamic light scattering. In TR-UIDLS, rigid arbitrary shaped nanoparticles characterized by two characteristic dimensions, are in Brownian motion in a solvent. They are illuminated by a vertically polarized focused Gaussian beam. A camera records the light scattered by the particles, in vertical-vertical polarization geometry and then in vertical-horizontal polarization geometry. By studying the light fluctuation recorded by the camera in both polarization geometries we can determine the mean values of the translational and rotational diffusion coefficients of the particles in the sample, using a model based on a rod-like/disk-like particle that is equivalent to the particles in the measurement volume during the time between when two consecutive pictures are taken. We have measured a sample of rod-like particles using scanning electron microscopy and compared the dimensions and aspect ratio with those measured using TR-UIDLS. The potential of TR-UIDLS to measure the distributions of translational and rotational diffusion coefficients, aspect ratio, length/thickness, and diameter of a polydisperse sample of nanoparticles is also discussed.

PhenoModifier: a genetic modifier database for elucidating the genetic basis of human phenotypic variation.

From clinical observations to large-scale sequencing studies, the phenotypic impact of genetic modifiers is evident. To better understand the full spectrum of the genetic contribution to human disease, concerted efforts are needed to construct a useful modifier resource for interpreting the information from sequencing data. Here, we present the PhenoModifier (https://www.biosino.org/PhenoModifier), a manually curated database that provides a comprehensive overview of human genetic modifiers. By manually curating over ten thousand published articles, 3078 records of modifier information were entered into the current version of PhenoModifier, related to 288 different disorders, 2126 genetic modifier variants and 843 distinct modifier genes. To help users probe further into the mechanism of their interested modifier genes, we extended the yeast genetic interaction data and yeast quantitative trait loci to the human and we also integrated GWAS data into the PhenoModifier to assist users in evaluating all possible phenotypes associated with a modifier allele. As the first comprehensive resource of human genetic modifiers, PhenoModifier provides a more complete spectrum of genetic factors contributing to human phenotypic variation. The portal has a broad scientific and clinical scope, spanning activities relevant to variant interpretation for research purposes as well as clinical decision making.

Analysis of economic burden and its associated factors of twenty-three rare diseases in Shanghai.

BACKGROUND: It is estimated that at present there are over 10 million rare disease patients in China. Recently an increased focus from policy perspective has been placed on rare diseases management. Improved disease definitions and the releases of local and national rare disease lists are some of the steps taken already. Despite these developments, few Chinese rare disease-related epidemiology and economic studies exist, thus hindering assessment of the true burden of rare diseases. For a rare disease with an effective treatment, this is a particularly important aspect due to the often-high cost associated. OBJECTIVE: The goal of this study is to address the data scarcity on the subject of rare diseases economic impact in China. We aim to address an existing knowledge gap and to provide a timely analysis of the economic burden of 23 rare diseases in Shanghai, China. METHODS: We utilized the data from the Health Information Exchange system of Shanghai and employed statistical modeling to analyze the economic burden of rare diseases with an effective treatment in Shanghai. RESULTS: First, we described the actual direct medical expenditure and analyzed its associated factors. Second, we found age, disease type, number of complications, and payment type were significantly associated with rare disease medical direct costs. Third, a generalized linear model was employed to estimate the annual direct cost. The mean direct medical cost was estimated as ¥9588 (US$1521) for inpatients and ¥1060 (US$168) for outpatients, and was over ¥15 million (~US$2.4 million) per year overall. CONCLUSION: Our study is one of the first quantifying the economic burden of an extensive set of rare diseases in Shanghai and China. Our results can serve to inform healthcare-focused policy making, contribute to the increase of public awareness, and incentivize development of rare-disease strategies and treatments specific to the Chinese context.

Influence of drugs on the prospective diagnostic method for coronary heart disease with urine.

The morbidity of coronary heart disease (CHD) with high risks has been rising in recent years. A novel and noninvasive method based on surface-enhanced Raman spectroscopy (SERS) was proposed by Yang et al. (Analyst 143: 2235, 2018) to prospectively diagnose the arterial blockage by detecting platelet-derived growth factor-BB (PDGF-BB) in urine. Clinically, anti-platelet drugs (such as aspirin, statins and clopidogrel) are often used for ordinary CHD patients or patients with percutaneous coronary intervention (PCI). Therefore, whether the previous developed method can be applied to the CHD patients on long-term medication (more than 6 months) or post-PCI patients was investigated here. Firstly, urine samples of 13 CHD patients on long-term medication (aspirin, rosuvastatin, clopidogrel bisulfate) and 13 post-PCI patients were measured by the proposed method. Clinical data of coronary angiography results provided by Xin Hua Hospital and Yangpu District Central Hospital Antu Branch revealed that these 26 patients were with serious arterial blockage, however, characteristic Raman peak at 1509 cm-1 attributed to PDGF-BB was not observed in the SERS spectra of these 26 patients. In addition, an eight-day follow-up investigation was performed on a CHD patient with PCI three years ago and on long-term medication. It was found that the Raman peak at 1509 cm-1 could be only observed in the third and fourth day after suspending the drugs. Furthermore, SERS spectra of mixed solutions of PDGF-BB and aspirin, rosuvastatin, mixed solutions of these two drugs and clopidogrel bisulfate were analyzed. The Raman peak at 1509 cm-1 was not found in all these spectra, it indicated that all the three kinds of drugs could influence on the SERS signal of PDGF-BB. Therefore, the previous developed method is not suitable for CHD patients on long-term medication and post-PCI patients.

Genomic analysis of a spinal muscular atrophy (SMA) discordant family identifies a novel mutation in TLL2, an activator of growth differentiation factor 8 (myostatin): a case report.

BACKGROUND: Spinal muscular atrophy (SMA) is a rare neuromuscular disorder threating hundreds of thousands of lives worldwide. And the severity of SMA differs among different clinical types, which has been demonstrated to be modified by factors like SMN2, SERF1, NAIP, GTF2H2 and PLS3. However, the severities of many SMA cases, especially the cases within a family, often failed to be explained by these modifiers. Therefore, other modifiers are still waiting to be explored. CASE PRESENTATION: In this study, we presented a rare case of SMA discordant family with a mild SMA male patient and a severe SMA female patient. The two SMA cases fulfilled the diagnostic criteria defined by the International SMA Consortium. With whole exome sequencing, we confirmed the heterozygous deletion of exon7 at SMN1 on the parents' genomes and the homozygous deletions on the two patients' genomes. The MLPA results confirmed the deletions and indicated that all the family members carry two copies of SMN2, SERF1, NAIP and GTF2H2. Further genomic analysis identified compound heterozygous mutations at TLL2 on the male patient's genome, and compound heterozygous mutations at VPS13A and the de novo mutation at AGAP5 on female patient's genome. TLL2 is an activator of myostatin, which negatively regulates the growth of skeletal muscle tissue. Mutation in TLL2 has been proved to increase muscular function in mice model. VPS13A encodes proteins that control the cycling of proteins through the trans-Golgi network to endosomes, lysosomes and the plasma membrane. And AGAP5 was reported to have GTPase activator activity. CONCLUSIONS: We reported a case of SMA discordant family and identified mutations at TLL2, VPS13A and AGAP5 on the patients' genomes. The mutations at TLL2 were predicted to be pathogenic and are likely to alleviate the severity of the male SMA patient. Our finding broadens the spectrum of genetic modifiers of SMA and will contribute to accurate counseling of SMA affected patients and families.

Noninvasive and prospective diagnosis of coronary heart disease with urine using surface-enhanced Raman spectroscopy.

A prospective diagnosis method for coronary heart disease (CHD) using human urine based on surface-enhanced Raman spectroscopy (SERS) is proposed, and could provide valuable information for judging whether to perform percutaneous coronary intervention (PCI) in clinics. Here, urine samples from 87 patients with CHD, including patients with PCI before operation (degree of cardiovascular congestion above 70%) and without PCI (degree of cardiovascular congestion under 70%), and 20 healthy humans were measured using SERS. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze the SERS spectra, revealing that the classification sensitivity and specificity were 90% and 78.9%, respectively, and the absolute value for loading of PC1 at 1509 cm-1 was the largest. Since platelet-derived growth factor-BB (PDGF-BB) is closely related to CHD, PDGF-BB aqueous solutions with various concentrations (1, 0.5, 0.1, 0.05 and 0.01 ppm) and a mixture of healthy human urine and PDGF-BB aqueous solutions were then investigated in this work, and it was found that the Raman peak at 1509 cm-1 may be attributed to PDGF-BB. Moreover, the measured SERS spectra of all the urine samples from the 87 patients with CHD were compared with the clinical data provided by a hospital, and it was revealed that the appearance of a peak at 1509 cm-1 in the SERS spectra was in good agreement with the results of coronary angiography tests when cardiovascular congestion was above 70%. This indicated that the classification sensitivity and specificity were 87.9% and 87.0%, respectively, through identification of the Raman peak at 1509 cm-1.

Right- and left-handed rules on the transverse spin angular momentum of a surface wave of photonic crystal.

By investigating the surface wave of photonic crystal, we put forward two sets of rules: the right-handed screw rule, judging the transverse spin angular momentum (SAM) directions according to the propagation direction of the surface wave; and the left-handed rule, judging the excitation direction of the surface wave in accordance to the SAM direction of incident circularly polarized light and the relative position of the dipole-like scatterer with respect to the interface where the surface wave propagates. Both right- and left-handed rules apply to the interface consisting of opposite-sign-permittivity materials. With the help of these two sets of rules, it is convenient to judge the direction of the transverse SAM and the excited surface wave, which facilitate the application involving transverse SAM of the surface wave.

Interrogating Patient-level Genomics and Mouse Phenomics towards Understanding Cytokines in Colorectal Cancer Metastasis.

Background: Colorectal cancer is the second leading cancer-related death worldwide and a majority of patients die from metastasis. Chronic intestinal inflammation plays an important role in tumor progression of colorectal cancer. However, few study works on systematically predicting colorectal cancer metastasis using inflammatory cytokine genes. Results: We developed a supervised machine learning approach to predict colorectal cancer tumor progression using patient level genomic features. To better understand the role of cytokines, we integrated the metastatic-related genes from mouse phenotypic data. In addition, pathway analysis and network visualization were also applied to top significant genes ranked by feature weights of the final prediction model. The combined model of cytokines and mouse phenotypes achieved a predictive accuracy of 75.54%, higher than the model based on mouse phenotypes independently (70.42%, p-value<0.05). In additional, the combined model outperformed the model based on the existing metastatic-related epithelial-to-mesenchymal transition (EMT) genes (75.54% vs. 71.61%, p-value<0.05). We also observed that the most important cytokine gene features of the our model interact with the cancer driver genes and are highly associated with the colorectal cancer metastasis signaling pathway. Conclusion: We developed a combined model using both cytokine and mouse phenotype information to predict colorectal cancer metastasis. The results suggested that the inflammatory cytokines increase the power of predicting metastasis. We also systematically demonstrated the critical role of cytokines in progression of colorectal tumor.

Simultaneous measurement of film thickness, temperature, and mass fraction of urea-water-solutions by multi-wavelength laser absorption spectroscopy.

Quantitative analysis for thickness, temperature, and mass fraction of liquid film is extremely crucial to the relevant industrial processes, but these parameters cannot be determined simultaneously by conventional measurement techniques. In the present work, a novel measurement method based on laser absorption spectroscopy was developed to measure the film temperature, thickness, and mass fraction of urea-water-solutions simultaneously by combining three wavelengths, 1420 nm, 1488 nm, and 1531 nm. Moreover, measurement accuracy of this method was validated by a calibration tool which provided liquid film with known film thickness, temperature, and mass fraction, respectively. It revealed that the deviation between the measured and known parameters with the developed method was 0.86%, 4.58%, and 3.85%, respectively.

A novel method for plastic particle sizing in suspension based on acoustic impedance spectrum.

The objective of this paper is to explore the relationship between the characteristics of plastic particles in suspension and acoustic impedance spectrum and to present a novel non-invasive methodology for both spherical and non-spherical particle sizing. By modifying the ultrasonic attenuation spectral model, theories relating acoustic impedance spectrum to particle characteristics have been established to implement quite a few numerical simulations for the first time, revealing that the acoustic impedance of plastic particles is sensitive to changes in particle concentration and size. Afterwards, experiments were carried out on polystyrene suspensions made by particles with different sizes. On the basis of the theoretical analysis, different transducers were employed over a frequency varied from 10MHz to 100MHz for different particle sizes respectively. Not only were spherical particles chosen for the experiment, but also non-spherical particles with three different size distributions considering the fact that practical particles have irregular shapes. All the samples were verified by optical microscope technique and their comparisons with the experimental results show that the plastic particles with different sizes are distinguishable by using acoustic impedance spectrum.

Predict drug permeability to blood-brain-barrier from clinical phenotypes: drug side effects and drug indications.

Motivation: Blood-Brain-Barrier (BBB) is a rigorous permeability barrier for maintaining homeostasis of Central Nervous System (CNS). Determination of compound's permeability to BBB is prerequisite in CNS drug discovery. Existing computational methods usually predict drug BBB permeability from chemical structure and they generally apply to small compounds passing BBB through passive diffusion. As abundant information on drug side effects and indications has been recorded over time through extensive clinical usage, we aim to explore BBB permeability prediction from a new angle and introduce a novel approach to predict BBB permeability from drug clinical phenotypes (drug side effects and drug indications). This method can apply to both small compounds and macro-molecules penetrating BBB through various mechanisms besides passive diffusion. Results: We composed a training dataset of 213 drugs with known brain and blood steady-state concentrations ratio and extracted their side effects and indications as features. Next, we trained SVM models with polynomial kernel and obtained accuracy of 76.0%, AUC 0.739, and F 1 score (macro weighted) 0.760 with Monte Carlo cross validation. The independent test accuracy was 68.3%, AUC 0.692, F 1 score 0.676. When both chemical features and clinical phenotypes were available, combining the two types of features achieved significantly better performance than chemical feature based approach (accuracy 85.5% versus 72.9%, AUC 0.854 versus 0.733, F 1 score 0.854 versus 0.725; P < e -90 ). We also conducted de novo prediction and identified 110 drugs in SIDER database having the potential to penetrate BBB, which could serve as start point for CNS drug repositioning research. Availability and Implementation: https://github.com/bioinformatics-gao/CASE-BBB-prediction-Data. Contact: rxx@case.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Explore Small Molecule-induced Genome-wide Transcriptional Profiles for Novel Inflammatory Bowel Disease Drug.

Inflammatory Bowel Disease (IBD) is a chronic and relapsing disorder, which affects millions people worldwide. Current drug options cannot cure the disease and may cause severe side effects. We developed a systematic framework to identify novel IBD drugs exploiting millions of genomic signatures for chemical compounds. Specifically, we searched all FDA-approved drugs for candidates that share similar genomic profiles with IBD. In the evaluation experiments, our approach ranked approved IBD drugs averagely within top 26% among 858 candidates, significantly outperforming a state-of-art genomics-based drug repositioning method (p-value < e-8). Our approach also achieved significantly higher average precision than the state-of-art approach in predicting potential IBD drugs from clinical trials (0.072 vs. 0.043, p<0.1) and off-label IBD drugs (0.198 vs. 0.138, p<0.1). Furthermore, we found evidences supporting the therapeutic potential of the top-ranked drugs, such as Naloxone, in literature and through analyzing target genes and pathways.

[Research on Radiation Spectrum of Pulverized Coal Combustion Flame].

In order to study on radiation spectrum of pulverized coal flame, radiation spectrums of pulverized coal flame on flat flame burner were measured with fiber optic spectrometer and the radiation characteristic was analyzed in detail. Distribution curves of radiation intensity of flame with wavelength were obtained based on Planck's Law and calibration by using blackbody furnace. Then, combustion parameters such as temperature and emissivity were calculated by using least square method. Therefore, the measurement method based on radiation spectrum of pulverized coal flame was proposed. And the experimental investigations on pulverized coal flame under different conditions were carried out with this method. The result shows that the radiation spectrum of pulverized coal flame is significant and continuous in the wavelength ranging from 200 to 1 100 nm. The temperature and emissivity of flame can be determined by using Planck's Law and least square method. Meanwhile, emission peaks of alkali metal such as sodium and potassium appear on the radiation spectrum of pulverized coal combustion flame near 590, 766, 769 and 779 nm, and the appearance of these emission lines is related with temperature. When the concentration of pulverized coal is increasing, the temperature of pulverized coal flame has a little change but the emissivity changes obviously. The intensity of radiation is increasing greatly with emissivity. It can provide important reference for combustion optimization of boiler.

Ultrafast image-based dynamic light scattering for nanoparticle sizing.

An ultrafast sizing method for nanoparticles is proposed, called as UIDLS (Ultrafast Image-based Dynamic Light Scattering). This method makes use of the intensity fluctuation of scattered light from nanoparticles in Brownian motion, which is similar to the conventional DLS method. The difference in the experimental system is that the scattered light by nanoparticles is received by an image sensor instead of a photomultiplier tube. A novel data processing algorithm is proposed to directly get correlation coefficient between two images at a certain time interval (from microseconds to milliseconds) by employing a two-dimensional image correlation algorithm. This coefficient has been proved to be a monotonic function of the particle diameter. Samples of standard latex particles (79/100/352/482/948 nm) were measured for validation of the proposed method. The measurement accuracy of higher than 90% was found with standard deviations less than 3%. A sample of nanosilver particle with nominal size of 20 ± 2 nm and a sample of polymethyl methacrylate emulsion with unknown size were also tested using UIDLS method. The measured results were 23.2 ± 3.0 nm and 246.1 ± 6.3 nm, respectively, which is substantially consistent with the transmission electron microscope results. Since the time for acquisition of two successive images has been reduced to less than 1 ms and the data processing time in about 10 ms, the total measuring time can be dramatically reduced from hundreds seconds to tens of milliseconds, which provides the potential for real-time and in situ nanoparticle sizing.