[Study on thermal decompositon properties of hexafluoropropane clean gaseous fire-extinguishing agent].

The thermal decomposition properties of hexafluoropropane clean gaseous fire-extinguishing agent were studied in tubular reactor from 500 to 750 degrees C and the decomposed gas was characterized by gas chromatography(GC), Fourier transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC-MS). Hydrogen fluoride was detected after the decomposed gas was analyzed by pH testing, while pentafluoropropylene was found by GC-MS. The results showed that hydrogen fluoride eliminated from hexafluoropropane was the main reaction, while pentafluoropropylene was the primary product during hexafluoropropane decomposition under high temperature. GC and FTIR results indicated that the reaction temperatures had significant effects on the thermal decomposition of hexafluoropropane. Haxafluropropane was steady at 500 degrees C, whereas started to decompose weakly at 600 degrees C. The degree of the thermal decomposition of hexafluoropropane was enhanced with the temperature increase. And hexafluoropropane underwent intense decompositon at 750 degrees C. FTIR can be used as a new method to study extinguishing mechanism of fluorine-containing fire extinguishing agent online.

[Effects of annual distribution difference of precipitation on evapotranspiration characteristics of dry orchard in Loess Plateau, China]. / é»„åœŸé«˜åŽŸé™æ°´å¹´å† åˆ†å¸ƒå·®å¼‚å¯¹æ—±ä½œæžœå›­è’¸æ•£ç‰¹å¾çš„å½±å“.

Precipitation is the main driving factor for hydrological cycle in rain-fed agricultural areas, which determines the water-related ecological environment and affects the evapotranspiration characteristics of crops. By analyzing the annual distribution characteristics of precipitation across different years, this study clarified the concentrated trend of precipitation in Jingning County. Based on a field experiment in 2018 and 2019, the changes of soil moisture with precipitation and the response process of orchard evapotranspiration characteristics to the annual distribution differences of precipitation were explored. The results showed that the concentration degree of precipitation was high in the study area over the years. The concentration period was mainly distributed in July and August, with the proportion of August being up to 75%. Moreover, the time of precipitation concentration period varied greatly among years. The response of soil moisture to precipitation was mainly concentrated in the 0-40 cm layer, while moisture in deep soil layer would change significantly only in response to heavy and continuous rain. Both 2018 and 2019 were water-rich years. The precipitation concentration degree in 2018 was high, and the concentration period was earlier and shorter, with the diurnal water consumption of apple trees showing a single peak with large amplitude. In 2019, the distribution of precipitation was uniform, the concentration period was late, and the diurnal water consumption showed double-peak with a small amplitude and a lagged large peak. The maximum water demand period of apple trees lasted a long time. The concentrated distribution of heavy rain in 2018 could not meet the physiological water demands of apple trees in the later period, which damaged the yield, and the utilization efficiency of precipitation decreased by 30.2% compared with 2019. In the Loess Plateau region, there is often a brief drought during the young fruit growth period, which would affect fruit quality. Therefore, water management during this period should be strengthened.

[Emission spectroscopy diagnostics of plasma electron temperature].

Electron temperature is one of the important parameters of plasma. It is very difficult to measure the electron temperature exactly and instantly owing to its complexity during discharge. As a plasma diagnostics technique, emission spectroscopy is widely applied in the study and diagnosis of any kind of plasma, because of its simple instrument system, noninterference of measurement, high sensitivity and fast responsibility. In the present paper, some methods for plasma electron temperature diagnosis, such as two lines method, multiline slope method, isoelectronic line method, Saha-Boltzmann equation, absolute intensity method, were introduced. And the applications of these methods were reviewed to provide reference for choosing appropriate methods in practice.

[The effect of resolution on the measurement of carbon bisulfide by DOAS].

Featuring excellent response characteristics and detection sensitivity and with much lower operational cost, differential optical absorption spectroscopy (DOAS) can be a powerful tool to trace the concentration variation of carbon bisulfide (CS2), a kind of poisonous gas with bad smell. In the DOAS measurement, the accuracy of the carbon bisulfide results is determined by the selected spectral resolution. The present paper focuses on the effect of resolution on the detected characteristic absorption structure of CS2 and the variation in differential cross section of CS2 with the change in resolution, from which the effect of resolution on the detection limit of CS2 is deduced. In the end, the functional dependence of the ratio of signal to noise (S/N) on the resolution is reduced by studying the relationship between light intensity and resolution. And the optimized resolution is determined as 0. 41-0. 50 nm (FWHM) with the lowest S/N. The optimized resolution range can yield high sensitivity, good selectivity and a reasonable time resolution for the accurate qualitative and quantitative analysis of CS2 (the results of five different concentration cells show linear correlation with r = 0.999 9 and all the relative standard deviations are less than 0.3% with n = 10). In the end, the long time, real time and continuous monitoring of CS2 was carried out in the Fengtai District of Beijing, and a good result was obtained.

[Multicomponent quantitative analysis using near infrared spectroscopy by building PLS-GRNN model].

The present paper introduces an application of near infrared spectroscopy(NIRS) multi-component quantitative analysis by building partial least squares (PLS)-generalized regression neural networks (GRNN) model. The PLS-GRNN prediction model for chlorine, fibre and fat in 45 feedstuff samples was established with good veracity and recurrence. Eight peak values in principal components compressed from original data by PLS and four in original spectra were taken as inputs of GRNN while 4 predictive targets as outputs. 0.1 was chosen as smoothing factor for its good approximation and prediction with the lowest error compared with 0.2, 0.3, 0.4 and 0.5. Predictive correlation coefficient and Standard error of the estimate of three components by the model are 0.984 0, 0.987 0 and 0.983 0, and 0.015 89, 0.154 1 and 0.115 1, while the Standard deviations of an unknown sample scanned 8 times are 0.003 26, 0.065 5 and 0.031 4. The results show that PLS-GRNN used in NIRS is a rapid, effective means for measuring chlorine, fibre in the fat in feedstuff powder, and can also be used in quantitative analysis of other samples. A settlement in the high error of prediction of other samples with lower contents was also shown.

[The rapid and nondestructive identification and infrared spectra visualization of Banlangen granule].

Banlangen granule is the main medicament of Banlangen, and is widely used for the prevention and cure of flu and viral infection. In the present article, Fourier transform infrared (FTIR) spectroscopy was used in the identification and analysis of the traditional Chinese medicine, Banlangen granule. The FTIR spectra of Banlangen granule samples coming from different companies and different batches of the same company were obtained. The curves of original absorbance and the second derivatives of the absorbance were analyzed for identification, and compared with those of indigowoad root and indigowoad leaf, which are their raw medicine materials. In addition, the infrared fingerprint spectra were visualized in the form of color bar figure. Results showed that characteristic fingerprint spectra of Banlangen Granule could be picked up effectively, and the products quality of different pharmaceutical factories and the spectra of different batch numbers from the same factory could be revealed directly by FTIR spectroscopy combined with the second derivative spectrum and fingerprint spectrum visualization of analytical data. Thus they were differentiated conveniently, accurately, quickly and directly with this method, providing a simple and effective technique in supervising and examining the quality of the traditional Chinese medicine.

[Determination of four contents of feedstuff powder using near infrared spectroscopy by PLS-BP model].

Partial least squares (PLS) and artificial neural networks (ANN) prediction model for four components of feedstuff has been established with good veracity and recurrence. The spectra put into the model should be processed by second derivative and standard normal variate (SNV). Ten principal components compressed from original data by PLS and two peak values were taken as the inputs of Back-Propagation Network (BP), while four predictive targets as outputs, according to Kolmogorov theorem and experiment, and twenty three nerve cells were taken as hidden nodes. Its training iteration times was supposed to be 10,000. Prediction deciding coefficient of four components by the model are 0.9950, 0.9980, 0.9990 and 0.9670, while the standard deviation of an unknown sample scanned parallelly are 0.02774, 0.04853, 0.03292 and 0.02204.

[Study on the multicomponent quantitative analysis using near infrared spectroscopy based on building elman model].

The present paper introduces an application of near infrared spectroscopy (NIRS) multi-component quantitative analysis by building a kind of recurrent network (Elman) model. Elman prediction model for phenylalanine (Phe), lysine (Lys), tyrosine (Tyr) and cystine (Cys) in 45 feedstuff samples was established with good veracity. Twelve peak value data from 3 principal components straight forward compressed from the original data by PLS were taken as inputs of Elman, while 4 predictive targets as outputs. Forty seven nerve cells were taken as hidden nodes with the lowest error compared with taking 43 and 45 nerve cells. Its training iteration times was supposed to be 1000. Predictive correlation coefficients by the model are 0.960, 0.981, 0.979 and 0.952. The results show that Elman using in NIRS is a rapid, effective means for measuring Phe, Lys, Tyr and Cys in feedstuff powder, and can also be used in quantitative analysis of other samples.

[Preparation and characterization of magnetic photocatalysts TiO2/Fe3O4].

Magnetic photocatalysts (TiO2/Fe3O4), which are liable to be separated and recovered, were prepared using Fe3O4 as the support. The surface morphology and crystal pattern were investigated by means of XRD, SEM, XPS and FTIR. The results showed that TiO2 wraps on the surface of Fe3O4. The photocatalytic activity of the catalysts was investigated by photocatalytic decolorization of (TNR). The photocatalytic activity of the catalysts is high and the separating ratio is good.

[Principal component extraction used for the interpretation of RS-FTIR spectra].

A method for interpretation of remote sensing FTIR spectra was set up based on ANN model. Considering long training time and over-fitting problem of ANN, two methods, partial least squares (PLS) and principal component analysis (PCA), were utilized to extract principal components of spectra, process time decrease from about 30 minutes to a few seconds. Meanwhile, the idea of calibration transfer was used to overcome the limitation of calibration model in remote sensing FTIR spectra analysis. With the optimization of ANN model, four-component mixtures of acetone, benzene, chloroform and methanol were predicted in a remote sensing and real-time way while the calibration model was built with EPA data. The best performance was yielded with PLS-ANN model, and the root mean square error (RMSE) of acetone, benzene, chloroform and methanol were 0.043, 0.031, 0.034 and 0.051 respectively, which confirm the real-time, correct and quick analysis of remote sensing FTIR in air monitoring.

[A review on applications of infrared spectroscopy to the study of traditional Chinese medicine].

Infrared spectroscopy is well suited for the rapid and nondestructive identification and quantification of traditional Chinese medicine (TCM) for its high resolution, easy operation and strong specialization of absorbance bands. It is becoming an efficient methodology in the quality control of TCM. The present paper reviews the applications of infrared spectrometry in the qualitative and quantitative analysis of TCM, especially to the discrimination and effective content determination of traditional Chinese medicinal materials (including different parts of the same medicinal material, TCM from different geographical areas, easily confusing TCM, and true and false TCM) and Chinese patent medicine (such as TCM formula particles, injections, etc.). With the ceaseless rapid development and popularization of FTIR spectrometer and computer science, infrared spectrometry will greatly accelerate the process of TCM modernization and internationalization by all means.

[Multi-component analysis of FTIR spectra of non-linear system using polynomial partial least squares method].

A non-linear algorithm, polynomial PLS was applied to the simultaneous analysis of OP-FTIR spectra of a five-component system whose FTIR spectra were seriously overlapped. The results were compared with the one obtained from PLS. PPLS yielded good performance, especially for the prediction of benzene and chloroform. RMSEP (root mean squared error of prediction) of benzene and chloroform in PPLS model were 0.043 and 0.087 and the corresponding values in PLS were 0.402 and 0.842, respectively. Meanwhile, variance was accounted by PPLS with fewer latent variables, which indicates the simplicity and robustness of the model. The successful application of PPLS to non-linear system was meaningful for the use of remote sensing FTIR in air monitoring.

[Automated recognition of VOCs using artificial neural networks].

Quantitative analysis of FTIR spectra, which are seriously overlapped in the spectral bands, was studied by artificial neural networks. The optimum network was chosen by a new criterion, i. e. the degree of approximation. After the network was established, two kinds of spectra were resolved. It was demonstrated that accurate results could be obtained when two components were both included. In addition, the unknown spectrum could be identified and quantified. It was showed that the artificial neural network has excellent non-linear ability of solution. Meanwhile, the method provides an efficient approach to the identification and quantification of the unknown samples.

[Remote passive detection of flame temperature of solid propellant adulterating nanoparticles].

The flame temperature of three kinds of solid propellants was measured by passive remote sensing FTIR with the resolution of 1 cm(-1). These three kinds of solid propellants are adulterate nano-scale metal oxide particles, adulterate normal metal oxide particles, and propellant without any adulterations. The main components of the solid propellant are nitrocellulose and nitroglycerin. The metallic oxides, including 6 nm CuO, 56 nm Fe2O3, 16 nm NiO, and correspondingly the normal particles, were adulterated into the solid propellants respectively. The flame temperature was calculated through the fine structure of the emission fundamental band of H2O at 2.75 microm. The results of the flame temperature of the solid propellants adulterating nano-scale CuO, Fe2O3 and NiO are 3089, 3193 and 3183 K, respectively. The temperatures of the three kinds of solid propellants were compared, and it was shown that there is no obvious difference in the flame temperature among the three kinds of solid propellants.

[Advanced development of remote sensing FTIR in air environment monitoring].

Remote sensing FTIR(RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It has several prevailing advantages: (1) high resolution and high selectivity; (2) it requiring no sampling and no sample preprocessing; (3) capability of detecting several compounds simultaneously; (4) real time, long distance, and automatic monitoring. So it is extraordinarily appropriate for the quantitative and qualitative determination, and remote, real time, dynamic monitoring of air contaminant, especially the toxic volatile organic compounds (VOCs). The present paper reviews the research progress in remote sensing FTIR for air monitoring made by the Laboratory of Advanced Spectroscopy in recent years, including chemometrics, computed tomography, FTIR spectra interpretation, spatial distribution of air pollutant concentration and passive remote monitoring, etc. These research findings show that the rapid development and application of remote sensing FTIR accelerated the temporal and spatial extension of analytical chemistry, and it will by all means have wide prospects in atmospheric environmental monitoring.

[Calibration transfer used for the interpretation of RS-FTIR spectrum].

A technique of analysis for RS-FTIR (Remote Sensing Fourier Transform Infrared) spectrum was built based on the principle of calibration transfer. A system of four-component gas mixtures, whose FTIR spectra were overlapped seriously, was determined as the object in the present research. Its FTIR data from EPA and field measurement were used to set up calibration and prediction models, respectively. With OSC (orthogonal signal correction), the model built with data of EPA predicted values of field measurement. The RMSEP values of acetone, methanol, benzene and chloroform were 0.0085, 0.0180, 0.0640 and 0.0028, respectively, while without OSC correction the corresponding values were 0.0856, 0.0479, 1.0653 and 0.0142, respectively. With optimization, the Kennard-Stone method was used for subset selection, and the iteration in the process of OSC was carried out 3 times, yielding the best performance. The result indicates that this technique can overcome the limitation of background and calibration model, which restricts the application of RS-FTIR in air monitoring.

[The calibration of instrument response function during passive FTIR measurement].

In order to obtain infrared spectral radiance distribution of some infrared sources, such as spectral radiant flux density, spectral radiant intensity, spectral radiance and spectral irradiance, the instrument response function under different conditions must be known. In the present paper, the calibration of instrument response function during passive FTIR measurement has been discussed. The experimental results illustrate that under different experimental conditions, the instrument response function varies not only with the temperature of the blackbody but also with the signal amplitude received by the infrared instrument. So, during passive FTIR measurement, the temperature and the emission signal amplitude of the source must be observed carefully in order to get satisfactory instrument response function.

[Studies on spectral characteristics of solid propellant by remote sensing FTIR].

The infrared spectral characteristics of high-intensity IR solid propellant were measured in this paper using a Bruker EQUINOX55 remote sensing FTIR spectrometer. The emission spectra of the combustion flame were recorded in the range of 4700-1800 cm(-1) with a spectral resolution of 4 cm(-1). The combustion temperatures of solid propellant at the burning time of 0, 9, 18, 27 and 36 s calculated from the molecular rotation-vibration spectra were 1992.5, 1610.9, 2294.4, 2361.1 and 1916.9 K, respectively. Moreover, the spectral radiance distributions of the high IR flare material at different times were given, and the combustion product concentrations of HCl, HF, CO2 and CO were determined quantitatively. Results showed that remote sensing FTIR is a potential technology that can be applied to the measurement of IR spectral characteristics, especially to the identification of the IR objects, guidance and anti-guidance in the military, and the modification of the make-up of solid propellant.

[Application and improvement of partial-least-squares in Fourier transform infrared spectroscopy].

Partial least squares(PLS) algorithm is an effective chemometric tool. It takes the advantages of multipal linear regression (MLR) and principal component regression (PCR), which makes Fourier transform infrared spectrometry (FTIR) more powerful and useful. Accompanied with increasing use of FTIR, the algorithm is modified and corrected under different circumstances. The applications of PLS to FTIR were mentioned. Improved algorithms were presented, such as moving windows PLS(MWPLS), robust PLS (RPLS), weighted PLS(WPLS), and non-linear PLS. Data pre-processing, selection of variable, noise elimination and non-linear model of PLS were introduced.

[Advances in passive OP-FTIR].

As an OP-FTIR(Open Path FTIR) technology, passive OP-FTIR not only has the advantages of OP-FTIR, but also has the ability to collect data from any direction without prior background information. This technology allows mobile, fast, man-held and stand-off detection of hazardous chemical cloud.The present paper presents some developments of passive OP-FTIR, including high altitude atmospheric pollution detection, auto-detection of toxic cloud, hot gases detection (such as the determination of major combustion products in aircraft exhausts, remote sensing of smoke plumes, and remote sensing of volcano emissions), temperature and combustion products determination (including cloud temperature determination). The present paper also gives the application of passive OP-FTIR in the military. With the development of FTIR and computer science, more and more applications of OP-FTIR to environment supervision, aviation and space flight, engine exhausts, combustion and military will be fulfilled.