Circular dichroism is a useful and popular technique to assay secondary structural content in proteins. CD measurements send right and left circularly polarized light into a sample compartment, and the difference in absorption of one polarization over the other (the ellipticity), due to the chirality of the sample, gives rise to the resulting spectrum. For proteins, the typical wavelength region examined by CD measurements is the far-UV (190-250 nm), where the peptide backbone electronic transitions n→π* and π→π* absorb. Typically, alpha helices have two minima at 222 and 209 nm, corresponding to n→π* and π→π* transitions. The lower wavelength transition is sensitive to helix length, where a shorter helix will result in a blueshift in the minimum. In contrast, beta-sheets have one minimum (n→π*) at ~215 nm, whereas random coil conformations have a single minimum (n→π*) at ~195 nm. Proteins containing mixtures of secondary structures have spectra that are difficult to interpret due to the fact that the helical transitions have a larger extinction coefficient than transitions of the other structural elements, and therefore signal from alpha-helices tend to dominate such spectra. Despite this, efforts have been made to quantify amounts of secondary structure directly from CD wavelength spectra using software such as K2D2, which compare CD spectra of proteins with unknown secondary structure contents to a library of spectra of proteins with known secondary structures. Some sidechains, most notably the aromatic ones, give rise to signals in CD wavelength spectra – tryptophan, for example, typically has a positive ellipticity at a wavelength of ~220 nm from its B b transition – however, the extinction of
Abstract. The Extended-range Atmospheric Emitted Ra- diance Interferometer (E-AERI) is a moderate resolution (1 cm −1 ) Fourier transform infrared spectrometer for mea- suring the absolute downwelling infrared spectral radiance from the atmosphere between 400 and 3000 cm −1 . The ex- tended spectral range of the instrument permits monitoring of the 400–550 cm −1 (20–25 µm) region, where most of the infrared surface cooling currently occurs in the dry air of the Arctic. Spectra from the E-AERI have the potential to provide information about radiative balance, trace gases, and cloud properties in the Canadian high Arctic. Calibra- tion, performance evaluation, and certification of the E-AERI were performed at the University of Wisconsin Space Sci- ence and Engineering Centre from September to October 2008. The instrument was then installed at the Polar Envi- ronment Atmospheric Research Laboratory (PEARL) Ridge Lab (610 m altitude) at Eureka, Nunavut, in October 2008, where it acquired one year of data. Measurements are taken every seven minutes year-round, including polar night when the solar-viewing spectrometers at PEARL are not operated.
Fig 3. Near infrared spectra of porcine skin left to hydrate for 30 minutes in water undergoing natural water desorption during a weight loss experiment. the spectra undergoing dehydration in Fig. 3, and are shown in Fig. 4. Here, a clear shift can be seen from 1949.8 nm to 1967.73 nm of the water combination band as the content of water within the skin sample is reduced although another study [ 6 ] indicates that the shift occurs from 1915 nm to 1940 nm. The spectrum with the largest peaks is the second derivative of the absorption spectrum obtained earlier where the sample had the highest water content or least dehydration period, whereas that with the smallest peaks belong to the absorption spectrum obtained when the sample was least hydrated. So, it follows that intensity decreased as the water content lessened but a wavelength shift is only apparent between 1949.8 and 1967.7 nm, and not on any of the other bands associated with NH, CH or CO bonds. As mentioned earlier, the weight of the sample declined with increased desorption time. So for each test of different hydration period, the weight of the sample was greatest when measured immediately after removing it from the jar containing water and this value decreased over time, and was
Koffi, B., Schulz, M., Bréon, F.-M., Dentener, F., Steensen, B. M., Griesfeller, J., Winker, D., Balkanski, Y., Bauer, S. E., Bel- louin, N., Bernsten, T., Bian, H., Chin, M., Diehl, T., Easter, R., Ghan, S., Hauglustaine, D. A., Iversen, T., Kirkevåg, A., Liu, X., Lohmann, U., Myhre, G., Rasch, P., Seland, Ø., Skeie, R. B., Steenrod, S. D., Stier, P., Tackett, J., Takemura, T., Tsi- garidis, K., Vuolo, M. R., Yoon, J., and Zhang, K.: Evalua- tion of the aerosol vertical distribution in global aerosol mod- els through comparison against CALIOP measurements: Aero- Com phase II results, J. Geophys. Res.-Atmos., 121, 7254–7283, https://doi.org/10.1002/2015JD024639, 2016.
explain the VIS/SWIR reflectance measurements well. In multilayer cloud systems with optically thin ice clouds over optically thick liquid clouds, shortwave reflectances could be sensitive to the lower cloud. Thus, the cloud phase determination can differ from that from TIR measurement, which is sensitive to the upper cloud. In the C6 shortwave algorithm result, the ice fraction changes abruptly at a CTT of about –33 °C (~240 K). According to Marchant et al. (2016), the CTT test uses a threshold at 240 K to “vote” for the ice clouds. This could result in the discontinuity of the ice fraction. As shown previously, cloud phase inference in ICAS is based on the consistency between the model and mea- surements under the constraints given by a priori know- ledge about the relationship between CTT and the phase, relying on BTDs in the split window with large weights. Thus, the cloud phase determination in ICAS is constrained strongly by the cloud phase dependence on the CTTs, which guarantees that ice clouds that are too warm and liquid clouds that are too cold are not re- trieved. However, the strong constraint on the cloud phase is a limitation of our algorithm because a small amount of information about the cloud phase comes from measurements. Baum et al. (2012) showed that in their algorithm refinement for MODIS C6, using cloud emissivity ratios between the split window bands sub- stantially improves the inference of the ice cloud phase, especially for optically thin ice clouds. In future work, cloud phase inference could be improved by using this index.
Abstract. The Tropospheric Monitoring Instrument (TROPOMI) spectrometer is the single payload of the Copernicus Sentinel 5 Precursor (S5P) mission. It measures Earth radiance spectra in the shortwave infrared spectral range around 2.3 µm with a dedicated instrument module. These measurements provide carbon monoxide (CO) total column densities over land, which for clear sky conditions are highly sensitive to the tropospheric boundary layer. For cloudy atmospheres over land and ocean, the column sensitivity changes according to the light path through the atmosphere. In this study, we present the physics-based operational S5P algorithm to infer atmospheric CO columns satisfying the envisaged accuracy (< 15 %) and precision (< 10 %) both for clear sky and cloudy observations with low cloud height. Here, methane absorption in the 2.3 µm range is combined with methane abundances from a global chemical transport model to infer information on atmo- spheric scattering. For efficient processing, we deploy a linearized two-stream radiative transfer model as forward model and a profile scaling approach to adjust the CO abundance in the inversion. Based on generic measurement ensembles, including clear sky and cloudy observations, we estimated the CO retrieval precision to be ≤ 11 % for surface albedo ≥ 0.03 and solar zenith angle ≤ 70 ◦ . CO biases of ≤ 3 % are introduced by inaccuracies in the methane a priori knowledge. For strongly enhanced CO concentrations in the tropospheric boundary layer and for cloudy conditions, CO errors in the order of 8 % can be introduced by the retrieval of cloud parameters of our algorithm. Moreover,
Normally in the thermoelastic measurement the calibration is based on the measurement of the deformation by a strain gauge rosette in a point of the structure. To put on the strain gauge rosette we have considered certain points with a high and regularly distributed solicitation. Repeated mea- surements by stain gauge of the principal strains and the rel- ative measure of the infrared intensity radiation allow us to estimate the calibration factor K, varying the applied load, the observation area and the excitation frequency. The best available estimate of the thermoelastic constant k value is k = 0.14 MPa mV −1 . The experimental standard deviation is s(k) = 0.01 MPa mV −1 .
mass spectrometer (QMS) 4–6 . In these experiments, the extent of desorption was derived only from the gas phase measurements. In contrast, infraredmeasurements can detect species on the substrate. Since chemical desorption is certainly a surface process, to monitor surface species by infraredmeasurements is crucial for a complete understanding of the chemical desorption phenomenon. In addition, time-resolved infraredmeasurements are necessary for obtaining chemical desorption cross-sections, which are highly desirable for chemical modelling studies. The present experiment, which takes an ideal reaction system where the reactant and product are both H 2 S,
The various techniques outlined above can be used to provide global climatologies as illu- strated in Fig. 4 (inside front cover). All sen- sors produce roughly the same geographical patterns ± showing the intertropical and South Pacific convergence zones, as well as midlati- tude storm tracks ± however, the rain magni- tude estimates for each region vary between techniques. The Global Precipitation Climatol- ogy Project is a major international initiative to combine the data from different sensors. To date it has concentrated on those datasets of long standing ± terrestrial raingauges, infraredmeasurements from geostationary satellites and passive microwave measurements from the Special Sensor Microwave/Imager instruments. The TRMM satellite, launched in 1997, brings a number of dedicated sensors to bear on the task, albeit just in tropical latitudes. The suite of instruments on board provide rainfall estimates through a number of active and passive microwave techniques, but to date climatologies based on these differ by up to 30% (Kummerow et al. 2000).
Control and data processing module is based on the 16-bit microcontroller (MCU) MSP430. The main feature of this MCU is an asynchronously clocking of MCU’s periphery modules. It allows to enter in low-power mode with power consumption <1 uA and wakeup by the external interruption. This allows creating of portable device with long time of battery life. MCU controls the operation of such functional parts as temperature measurements, user interface, data processing, power management and wireless communication with PC. The digital filter and integrator provides the optimal filtering of signal and its integration with different time constant.
The infrared thermometry can be further divided into two temperature measurement methods of infrared imaging temperature measurement and infrared probe temperature measurement. Both methods are non-direct contact measurement methods. The infrared imaging temperature measurement is based on the principle of black-body radiation law. Any object higher than absolute zero degree can emit radiant energy. The temperature on the surface of the object is reflected by the size of object infrared radiation energy and distribution of wavelength. Therefore, the surface temperature of the object can be determined by measuring the infrared radiation energy size of the object. The sensor is used for detecting the infrared radiation emitted by the object, and the radiation energy can be turned into electrical signals. The temperature on the surface of the tested object can be obtained finally through calibration operation [ 1 ].
The infrared spectra of the complexes have been studied to characterize their structures. The IR spectra of the complexes register ν (C-O) at about 1324-1340 cm -1 [6,13-14]. In the Schiff base ν (C=N) stretching band at 1610 cm -1 . This band shifts to lower energy by 10 to 30 cm -1 in chelates indicating co-ordination through the azomethine nitrogen [15-22]. The sharp bend in the range 750-780 cm -1 and 1525-1535 cm -1 are due to aromatic ν (C-H) [6,23] and ν (C=C) [6,24] respectively. The frequencies in the range 1145-1165 cm -1 attributed to ν (C-N) stretching . Conclusive evidence of the bonding is also shown by the observation that new bonds in the spectra of the metal complexes appears at 455-460 cm -1 and 514-525 cm -1 these are assigned to ν (M-O) and ν (M-N) stretching vibrations and are not observed in the spectra of the ligand [26- 30]. The presence of sharp band corresponding to the remaining hydroxyl group at 3400 cm -1 but it is obscured by the presence of water molecules bands. This was appeared for the most complexes and a very broad band at about 3100-3500 cm -1 region, which was associated with coordinated or solvent water molecules .
245 °C. Based on the DSC curves (Fig. 8b), the Tg of the cured polymer at 245 °C for 10 min increased to 87 °C owing to the increase in the molecular weight of the resin system as a consequence of crosslinking reactions. In addition, there was no endother- mic event in the range of 140–240 °C indicating that the resin was fully cured after the thermal treatment. Based on these experiments, the cure temperature information was obtained, and some samples were prepared for evaluating curing reactions using near- infrared spectroscopy since the changes upon heating are not easily detected using mid- infrared spectroscopy due to overlapping and low intensities of vibrational bands .
Fourier transform infrared (FTIR) measurements were carried out to identify the possible biomolecules responsible for reduction, capping and efficient stabilization of molybdenum NPs and the local molecular Environment of the capping agents on the nanoparticles .FT-IR spectrum of the Plant extract is represented in figure-4 shows the absorption band at the range of primary alcohols show a strong band near 1018.9cm-1 ,1617cm -1 C=C stretching bands and the absorption bands are near 1375cm -1 show the presence tertiary butyl group is the IR of plant sample is reduced to one sharp and intense peak at the range of 1389cm -1 in the IR spectrum of Mo nanoparticles synthesized by green method figure-5.This is evident for the conversion of molybdenum to Nano sized molybdenum. The IR spectrum chemically synthesized nanoparticles represented as figure-6. The band at 3017.8cm-1 corresponds to the C-H stretching of alkenes, the N-H deformation vibration for amino salts appear as
In contrast, satellite-based infrared (IR) emission measure- ments provide a global coverage at day- and nighttime during all seasons. Furthermore, IR nadir instruments have a better global and temporal coverage than ultraviolet (UV)/visible (VIS) nadir measurements or occultation measurements. IR nadir measurements have a long-standing history in detect- ing aerosols and retrieving aerosol composition and micro- physics. The aerosol measurements from IR nadir instru- ments mainly focus on volcanic ash (e.g. Prata, 1989a; Gue- henneux et al., 2015), mineral dust (e.g. Peyridieu et al., 2010; Klüser et al., 2011; Klueser et al., 2012; Liu et al., 2013), and smoke (Fromm et al., 2008). There are several methods available to detect aerosol, filter out ice clouds, and classify aerosol types. These methods comprise the split win- dow/reverse absorption technique for volcanic ash (Prata, 1989a, b; Rose et al., 2013), trispectral approaches for vol- canic ash and mineral dust (Ackerman et al., 1990; Acker- man, 1997; Guehenneux et al., 2015), and multispectral ap- proaches for hyperspectral instruments (Gangale et al., 2010; Clarisse et al., 2010, 2013). Although the established meth- ods are used for operational data products they are still sub- ject to improvements (Guehenneux et al., 2015). The capa- bility of detecting sulfate aerosol with IR nadir measure- ments has been demonstrated for band measurements (Baran et al., 1993; Ackerman, 1997) and for hyperspectral instru- ments (Clarisse et al., 2010; Gangale et al., 2010; Karagulian et al., 2010). Ackerman (1997) found that sulfate droplets with an aerosol optical depth (AOD) larger than 0.01 at 11 µm should be detectable from IR nadir measurements. However, a major disadvantage of IR nadir aerosol measurements is
The first types of satellite measurements of volcanic clouds used visible imagery to infer the spatial extent and movement of clouds. Infrared window (7–14 µm) measure- ments were used to derive cloud top temperatures and then cloud top height by assuming the clouds to be opaque and by using a nearby radiosonde measurement of the change of atmospheric temperature with height. With improvements in satellite instruments (more spectral channels, better accu- racy and spatial resolution) it was demonstrated that two in- frared channels centered near 11 and 12 µm could be used to uniquely identify silicate-bearing ash (Prata, 1989a, b). Further work (Wen and Rose, 1994; Prata and Grant, 2001) showed that by utilizing microphysical and radiative transfer models it was possible to retrieve fine-ash (1–10 µm, radius) particle size, optical depth and subsequently mass concentra- tions.
In this study, two groups of sealants including He- lioseal clear and opaque (IvoclarVivadent, Liechtens- tein) were used. The teeth were randomly divided into two groups (43 teeth in each group) according to the table of random numbers. Helioseal clear sealant was applied on 122 sites of 13 sound and 30 carious teeth, while 123 sites of 15 non-carious and 28 carious teeth were sealed with Helioseal opaque sealants. Before the sealant application, the occlusal surface of each tooth was etched for 30 seconds with 37% phosphoric acid gel (IvoclarVivadent, Liechtenstein). Then, the teeth were rinsed with air-water syringe for 15 seconds. Fi- nally, the occlusal surfaces of the teeth were dried by air spray and then sealants were applied on prepared sur- faces. Subsequently, sealants were cured for 40 seconds by a light-curing device (Litex 680A/USA, 400mw/ cm 2 ). After curing, DIAGNOdent device was utilized again for measurements.
Because of the heterogeneous distribution of trash type and size, the use of different sampling specimens during three independent measurements, and the availability of all three instruments (especially SA) at any one cotton fiber research facility, there are few literature studies that compare trash readings among the three methods. In addition, because of relatively small sample size (~0.5 g) used in AFIS procedure, trash readings from HVI and SA have been frequently cited. Because USDA AMS has regulated the HVI trash index as a global fiber quality characteristics, there is interest from domestic and foreign customers to relate the geometric-based HVI trash readings with the gravimetric-based SA values. Given the complex- ity of trash in lint fibers and the nature of HVI and SA measurements, it is a challenge to unravel the relation- ship between the two types of trash determination.
As discussed earlier, the LED source produces two types of heating effects due to conducted and radiated energy. Conducted heat due to the semiconductor junction is measured in-vitro using a NIR-pass filter as shown in fig- ure 2. In this setup, measurements are performed with and without NIR-pass filter. The NIR-pass filter does not heat up due to NIR light absorption since it is transparent to NIR light. When filter is used, the thermocouple meas- ures the temperature increase due to NIR light absorption. When filter is not used, the temperature increase is caused by the combined heating effect of semiconductor junction and NIR light absorption. Therefore, the difference between two readings gives the temperature increase solely due to semiconductor junction heating.
We analyzed the main factors driving the calculation of the tangent heights of spaceborne limb measurements. We found that the factor with largest effect in the tangent height cal- culation is the assumed atmosphere. Using a climatological model in place of the real atmosphere may cause tangent height errors up to ± 200 m. This error is on the same or- der of magnitude of the most recent estimates of the error on MIPAS tangent height due to uncertainties on the pointing angles and satellite attitude. In MIPAS retrievals, this inac- curacy causes a temperature error on the order of the noise error if the tangent height is adjusted by fitting the tangent pressure (this is the case of the ESA algorithm). However, if the retrieval assumes a fixed tangent pressure, the inaccu- racies in tangent heights may cause temperature differences locally exceeding 4–5 K.