Spectral Match Research Articles

IOC adding new processing unit at Haldia refinery

Fragmentation of simple sugars, using fluidized thermal bed cracking, results in a mixture that contains a variety of polar oxygenated compounds, some yet unknown to the industries. This lack of understanding of the product composition, hinders the downstream processes in conversion of sugars to useful chemicals. The aim of this study was fourfold: to develop a GC–MS identification workflow for carbohydrate fragmentation compounds, to build a library of compounds detected by five GC–MS methods including liquid injection, liquid-liquid extraction, static headspace, solid-phase microextraction and derivatization, to investigate and compare the compound coverage of the five methods, and finally investigate possible reaction pathways for some of the analytes in the mixture. The analysis was done on a mixture of seven representative sugar fragmentation samples. The identification workflow was based on mass spectral match factors (>80 %), retention indices and analytical standards. A total of 389 compounds were detected, out of which, 46 compounds were fully identified (through confirmatory analysis of analytical standards), 87 were tentatively identified (spectral match factors above 80 %), 71 were un-identified with spectral match factors of below 80 % and 185 were discarded (contaminants in blanks etc.). The production pathway for some of the identified compounds in the sugar conversion mixtures were also discussed. In conclusion, solid-phase microextraction detected the highest number of analytes (93), mostly volatile organic compounds and the derivatization GC–MS technique detected the lowest number of analytes (19) but was the only method to detect the largest molecules (C6-C12) in size. This library of CF products can be used as a suspect-screening database for other biomass fragmentation mixtures.

TCAD based performance assessment of Indium Gallium Nitride based single junction solar cells for different mole fractions of Indium

The tailoring the band gap energy of the ternary Indium Gallium Nitride (InxGa(1−x)N) alloy shows a good spectral match with a range of wavelength in electromagnetic spectrum and provided a new approach for its utilization in Photovoltaic solar cells. In this paper a 2D numerical simulation of Indium Gallium Nitride (InxGa1−xN) single junction solar cell using TCAD has been presented. The device has been simulated and analyzed using physical models such as Auger recombination models using Fermi–Dirac Statistics, Shockley–Read–Hall recombination models and Band Gap Narrowing effect. The J–V and P–V characteristics of the device in dark and illuminated condition were analyzed. The single-junction In0.72Ga0.28N solar cell shows the maximum efficiency under normalized AM 1.5 G, 0.1 W/cm2 and at a temperature of 300 K. Further, figure of merits depending on different mole fraction of Indium, like short current density (Jsc), Open circuit Voltage (Voc), fill factor (FF), maximum power generated (Pmax) and conversion efficiency of the designed solar cell has been extracted.

Fatty Acid Composition by Total Acyl Lipid Collision-Induced Dissociation Time-of-Flight (TAL-CID-TOF) Mass Spectrometry.

Totalacyllipid collision-induced dissociation time-of-flight (TAL-CID-TOF) mass spectrometry uses a quadrupole time-of-flight (QTOF) mass spectrometer to rapidly provide a comprehensive fatty acid composition of a biological lipid extract. Samples are infused into a QTOF instrument, operated in negative mode, and the quadrupole is used to transfer all, or a wide mass range of, precursor ions to the collision cell for fragmentation. Time-of-flight-acquired mass spectra provide mass accuracy and resolution sufficient for chemical formula determination of fatty acids in the complex mixture. Considering the limited number of reasonable CHO variants in fatty acids, one can discern acyl anions with the same nominal mass but different chemical formulas. An online application, LipidomeDB Data Calculation Environment, is employed to process the mass spectral output data and match identified fragments to target fragments at a resolution specified by the user. TAL-CID-TOF methodology is a useful discovery or screening tool to identify and compare fatty acid profiles of biological samples.

Assessment of emerging polar organic pollutants linked to contaminant pathways within an urban estuary using non-targeted analysis.

A comprehensive, non-targeted analysis of polar organic pollutants using high resolution/accurate mass (HR/AM) mass spectrometry approaches has been applied to water samples from San Francisco (SF) Bay, a major urban estuary on the western coast of the United States, to assess occurrence of emerging contaminants and inform future monitoring and management activities. Polar Organic Chemical Integrative Samplers (POCIS) were deployed selectively to evaluate the influence of three contaminant pathways: urban stormwater runoff (San Leandro Bay), wastewater effluent (Coyote Creek, Lower South Bay), and agricultural runoff (Napa River). Grab samples were collected before and after deployment of the passive samplers to provide a quantitative snapshot of contaminants for comparison. Composite samples of wastewater effluent (24 hours) were also collected from several wastewater dischargers. Samples were analyzed using liquid-chromatography coupled to high resolution mass spectrometry. Resulting data were analyzed using a customized workflow designed for high-fidelity detection, prioritization, identification, and semi-quantitation of detected molecular features. Approximately 6350 compounds were detected in the combined data set, with 424 of those compounds tentatively identified through high quality spectral library match scores. Compounds identified included ethoxylated surfactants, pesticide and pharmaceutical transformation products, polymer additives, and rubber vulcanization agents. Compounds identified in samples were reflective of the apparent sources and pathways of organic pollutant inputs, with stormwater-influenced samples dominated by additive chemicals likely derived from plastics and vehicle tires, as well as ethoxylated surfactants.

Polymer Type Identification of Marine Plastic Litter Using a Miniature Near-Infrared Spectrometer (MicroNIR)

Plastic pollution in the marine environment has turned into an important research topic in recent decades. Until recently, studies were often based on visual assessment only, which is not enough to draw any conclusion about the chemical nature of found plastic items and could lead to incorrect results. Standardized, fast, and efficient low-cost methods for marine plastic litter identification are urgently needed to monitor the occurrence and distribution worldwide. In this paper, we demonstrate that a miniaturized handheld near-infrared spectrometer—MicroNIR—can be used for on-site identification of different plastic polymers. A database containing polymer spectra of the most produced and reported polymer types in the marine environment was created including polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), polyamide (PA), polycarbonate (PC), polyurethane (PUR), and Silicone. Using spectral match value (SMV, included in the instrument software) for spectra analysis resulted in an accurate classification of all nine polymer types. The method was used for the identification of marine macro-, meso-, and microplastic litter collected on beaches in sediments and seawater and enabled the correct identification of marine plastic litter for macro-, meso- (96%), and microplastics (73%) with exception of totally black items and items less than 1 mm in size. The method and instrumentation presented here are very well suited to support “Citizen Science” marine litter monitoring projects during beach cleaning and similar activities.

A Mass Spectrometry Based Metabolite Profiling Workflow for Selecting Abundant Specific Markers and Their Structurally Related Multi-Component Signatures in Traditional Chinese Medicine Multi-Herb Formulae.

In Traditional Chinese Medicine (TCM), herbal preparations often consist of a mixture of herbs. Their quality control is challenging because every single herb contains hundreds of components (secondary metabolites). A typical 10 herb TCM formula was selected to develop an innovative strategy for its comprehensive chemical characterization and to study the specific contribution of each herb to the formula in an exploratory manner. Metabolite profiling of the TCM formula and the extract of each single herb were acquired with liquid chromatography coupled to high-resolution mass spectrometry for qualitative analyses, and to evaporative light scattering detection (ELSD) for semi-quantitative evaluation. The acquired data were organized as a feature-based molecular network (FBMN) which provided a comprehensive view of all types of secondary metabolites and their occurrence in the formula and all single herbs. These features were annotated by combining MS/MS-based in silico spectral match, manual evaluation of the structural consistency in the FBMN clusters, and taxonomy information. ELSD detection was used as a filter to select the most abundant features. At least one marker per herb was highlighted based on its specificity and abundance. A single large-scale fractionation from the enriched formula enabled the isolation and formal identification of most of them. The obtained markers allowed an improved annotation of associated features by manually propagating this information through the FBMN. These data were incorporated in the high-resolution metabolite profiling of the formula, which highlighted specific series of related components to each individual herb markers. These series of components, named multi-component signatures, may serve to improve the traceability of each herb in the formula. Altogether, the strategy provided highly informative compositional data of the TCM formula and detailed visualizations of the contribution of each herb by FBMN, filtered feature maps, and reconstituted chromatogram traces of all components linked to each specific marker. This comprehensive MS-based analytical workflow allowed a generic and unbiased selection of specific and abundant markers and the identification of multiple related sub-markers. This exploratory approach could serve as a starting point to develop more simple and targeted quality control methods with adapted marker specificity selection criteria to given TCM formula.

Design of efficient radiative emission and daytime cooling structures with Si3N4 and SiO2 nanoparticle laminate films.

Research on radiative cooling has attracted recent widespread interest owing to the potential for low-cost passive structures to enable large-scale thermal energy management. Using a generalized effective medium theory, we theoretically show that two-layer films comprised of SiO2 and Si3N4 nanoparticle layers on an Ag back reflector exhibit superior radiative cooling compared to single-layer or two-layer dense solid films, and can outperform other reported designs. The performance enhancement is a result of the ability to tune the nanoparticle fill fraction, which improves the spectral match between emissivity of this structure and the atmospheric transmission window. We also propose a standardized method for comparing the performance of radiative cooling structures reported by the research community.

Facile preparation of core-shell Ag@SiO2 nanoparticles and their application in spectrally splitting PV/T systems

Several researchers have demonstrated that plasmonic nanofluids based filters can potentially enhance hybrid solar photovoltaic/thermal (PV/T) systems performance. In this work, we report a facile process for achieving silica-coated silver (Ag) nanoparticles using dimethylamine (DMA) as a basic solvent to induce tetraethyl orthosilicate (TEOS) hydrolysis. Then, the prepared Ag@SiO2 nanoparticles which have a controllable silica shell thickness are suspended in propylene glycol-CoSO4 hybrid fluid. Finally, the characteristics of the PV/T system filtered by the Ag@SiO2/CoSO4-PG nanofluids are evaluated based on the indoor test and photo-thermal conversion model. The results show that the nanoparticles prepared under optimized conditions, i.e., pH value of 8–9, water: ethanol volume ratio of 1:4, temperature of 25 °C, TEOS volume of 0.05 mL, and reaction time of 12 h, exhibit an absorption peak at 474 nm. Further, this study reveals that Ag@SiO2/CoSO4-PG nanofluid filter with concentration of 25.4 mg/L gives a reasonable spectral match with silicon concentrator solar cell according to the measured optical transmittance and the calculated filtering efficiency of 39.3%. The use of 25.4 mg/L Ag@SiO2/CoSO4-PG nanofluid filter produces a higher total efficiency of 63.3% and yields economic value enhancement of 67.8% compared to the PV only system when worth factor (w) is 3.

Methodology: an optimized, high-yield tomato leaf chloroplast isolation and stroma extraction protocol for proteomics analyses and identification of chloroplast co-localizing proteins

BackgroundChloroplasts are critical organelles that perceive and convey metabolic and stress signals to different cellular components, while remaining the seat of photosynthesis and a metabolic factory. The proteomes of intact leaves, chloroplasts, and suborganellar fractions of plastids have been evaluated in the model plant Arabidopsis, however fewer studies have characterized the proteomes of plastids in crops. Tomato (Solanum lycopersicum) is an important world-wide crop and a model system for the study of wounding, herbivory and fruit ripening. While significant advances have been made in understanding proteome and metabolome changes in fruit ripening, far less is known about the tomato chloroplast proteome or its subcompartments.ResultsWith the long-term goal of understanding chloroplast proteome dynamics in response to stress, we describe a high-yielding method to isolate intact tomato chloroplasts and stromal proteins for proteomic studies. The parameters that limit tomato chloroplast yields were identified and revised to increase yields. Compared to published data, our optimized method increased chloroplast yields by 6.7- and 4.3-fold relative to published spinach and Arabidopsis leaf protocols, respectively; furthermore, tomato stromal protein yields were up to 79-fold higher than Arabidopsis stromal proteins yields. We provide immunoblot evidence for the purity of the stromal proteome isolated using our enhanced methods. In addition, we leverage our nanoliquid chromatography tandem mass spectrometry (nanoLC–MS/MS) data to assess the quality of our stromal proteome. Using strict criteria, proteins detected by 1 peptide spectral match, by one peptide, or were sporadically detected were designated as low-level contaminating proteins. A set of 254 proteins that reproducibly co-isolated with the tomato chloroplast stroma were identified. The subcellular localization, frequency of detection, normalized spectral abundance, and functions of the co-isolating proteins are discussed.ConclusionsOur optimized method for chloroplast isolation increased the yields of tomato chloroplasts eightfold enabling the proteomics analysis of the chloroplast stromal proteome. The set of 254 proteins that co-isolate with the chloroplast stroma provides opportunities for developing a better understanding of the extensive and dynamic interactions of chloroplasts with other organelles. These co-isolating proteins also have the potential for expanding our knowledge of proteins that are co-localized in multiple subcellular organelles.

A SPECTRALLY IMPROVED POINT CLOUD CLASSIFICATION METHOD FOR MULTISPECTRAL LIDAR

Abstract. Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification.

Substandard Quality of the Antimicrobials Sold in the Street Markets in Haiti.

This pilot study was conducted to analyze the quality of the antimicrobials sold in the street markets in Port-au-Prince, Haiti. A total of 258 packs containing antimicrobials were bought in 28 street markets in Port-au-Prince (Haiti). Tablets and contents of capsules included in 196 packs were analyzed using a Raman handheld spectrometer (NanoRAM of BWTEK, Model: BWS456-785) during the first quarter of 2019. Three out of 11 antimicrobials (Amoxicillin, Metronidazole, and Cotrimoxazole) had a high spectral match with an HQI ≥ 90 to the respective authentic medicine for more than 95% of their tablets/capsules. For six antimicrobials (Tetracycline, Erythromycin, Cloxacillin, Azithromycin, Clarithromycin, and the combination Amoxicillin + Clavulanic Acid) none of their tablets/capsules showed a sufficient spectral match with the authentic medicine. This finding indicates that these products sold in the markets did not contain the labeled drug and/or contained a degraded drug. In addition to the fact that prescription antimicrobials can be purchased in street markets, the present field study found that for most of them (including “Watch” antimicrobials according to the AWaRe classification) were substandard, which contributes to the present antimicrobials resistance epidemic.

LED Solar Spectrum Computer Simulation Based on Non-dominated Sorting Genetic Algorithm

The solar computer simulation technology has been extensively applied in the fields of polymer curing test, solar cell detection and calibration, and satellite heat balance test, etc. The non-negative least-squares solution of the overdetermined equations based on the non-dominated sorting genetic algorithm (NSGA) to optimize the monochromatic light-emitting diode (LED) matching light source combination, replace some monochromatic LED with white LED to simulate the solar spectrum, and discuss several LED lights with different peak wavelengths that can be replaced by white LED. The simulation results suggest that in the range of 300 ∼ 1100 nm, as the monochromatic LED types replaced by white LED increase, the total number of LEDs used is reduced, and the spectral match decreases. When LEDs with three different peak wavelengths are replaced, the correlation index in the fitting based on the algorithm is 0.9035, where the number of LEDs can be reduced by 15.6%, and the simulation spectrum is basically consistent with the target one. The proposed method has a small spectral mismatch and can be used to distinguish two absorption valleys of the standard solar spectrum AM1.5 accurately.

Geomagnetically Induced Current Model Validation From New Zealand's South Island

AbstractGeomagnetically induced currents (GICs) during a space weather event have previously caused transformer damage in New Zealand. During the 2015 St. Patrick's Day Storm, Transpower NZ Ltd has reliable GIC measurements at 23 different transformers across New Zealand's South Island. These observed GICs show large variability, spatially and within a substation. We compare these GICs with those calculated from a modeled geolectric field using a network model of the transmission network with industry‐provided line, earthing, and transformer resistances. We calculate the modeled geoelectric field from the spectra of magnetic field variations interpolated from measurements during this storm and ground conductance using a thin‐sheet model. Modeled and observed GIC spectra are similar, and coherence exceeds the 95% confidence threshold, for most valid frequencies at 18 of the 23 transformers. Sensitivity analysis shows that modeled GICs are most sensitive to variation in magnetic field input, followed by the variation in land conductivity. The assumption that transmission lines follow straight lines or getting the network resistances exactly right is less significant. Comparing modeled and measured GIC time series highlights that this modeling approach is useful for reconstructing the timing, duration, and relative magnitude of GIC peaks during sudden commencement and substorms. However, the model significantly underestimates the magnitude of these peaks, even for a transformer with good spectral match. This is because of the limited range of frequencies for which the thin‐sheet model is valid and severely limits the usefulness of this modeling approach for accurate prediction of peak GICs.

Adjustable high-power-LED solar simulator with extended spectrum in UV region

This article describes the design of a tunable, versatile, light emitting diode (LED) based solar simulator that employs 19 different wavelengths controlled independently by high power LEDs, covering 250–1000 nm standard solar spectrum of AM1.5Global. The main aspects of design procedure were addressed, including the LEDs spatial arrangement, intensity control, thermal management and secondary optics. According to International Electrotechnical Commission (IEC) and American Society for Testing and Materials (ASTM) standards on solar simulators rating, spectral match, irradiance non-uniformity and temporal instability of the solar simulator were investigated. The proposed solar simulator meets the Class AAA specifications in the all three categories for the test plane of 2.3×2.3 cm at a distance of 8.7 cm from the LEDs. The spectral tunability and extended spectrum of this solar simulator make it a useful tool in research studies of different fields, especially for testing of newly photovoltaic technologies with extended spectral responsivity in UV region such asultraviolet responsive solar cells.

Dynamic pressure gradient modulation for comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection

Dynamic pressure gradient modulation (DPGM) is investigated for comprehensive two-dimensional gas chromatography (GC × GC) with time-of-flight mass spectrometry (TOFMS) detection. With DPGM, a commercial pneumatic “pulse” valve is opened to introduce a suitably high auxiliary gas pressure at a T-junction connecting the first dimension (1D) and second dimension (2D) columns during the modulation period (PM), temporarily stopping the 1D flow. The valve is then closed for the duration of a pulse width (pw) to “re-inject” temporally focused 1D eluate onto the 2D column for separation. This flow modulation technique is observed to be compatible with TOFMS detection using a 2D flow rate of 4 ml/min for the separation of a 90-component test mixture. A 25 min separation window using a PM = 1 s and pw = 200 ms for full modulation (and 100% duty cycle) provided an average 1Wb = 4.5 s and 2Wb = 130 ms for a 2D peak capacity of nc,2D = 2700 (100 peaks per min). The detector response enhancement factor (DREF) serves as a metric for the enhanced sensitivity of the modulated relative to the unmodulated 1D peaks, with DREFs ranging between 10 and 20 and about a 5-fold improvement in signal-to-noise ratio (S/N). The bilinear “quality” of the GC × GC data is studied using the chemometric method parallel factor analysis (PARAFAC). Since PARAFAC requires sufficiently trilinear data, the reproducibility of the 2D peak shape for a given analyte is confirmed using lack-of-fit (LOF) and percent variation (R2) metrics. The limit-of-detection (LOD) for the representative analyte hexadecane is determined using PARAFAC, providing an LOD of 0.7 ppb (±0.03 ppb) for three replicates. Seven heavily overlapped analytes are also fully resolved by PARAFAC down to the part-per-million (ppm) concentration level, producing reproducible spectra with a majority of spectral match values (MV) over 800 (RSD ≤ 7.1%). This study provides promising results for DPGM as a flow modulation technique compatible with GC × GC-TOFMS, providing high sensitivity data suitable for chemometric analysis.

A novel PARAFAC model for continental hot springs reveals unique dissolved organic carbon compositions

Dissolved organic carbon in hot springs reflects a range of sources and biogeochemical processes. We evaluated ~200 continental hot spring samples, with a range in pH and temperature, collected from the Tengchong hydrothermal region, Yunnan Province, China and Yellowstone National Park, Wyoming, USA. Dissolved organic carbon concentrations ranged from 16.7 µM to 2.97 mM. Acidic springs displayed the highest values and widest range in carbon concentration. Alkaline springs had a narrower range and lower average concentrations. Carbon composition was evaluated using ultraviolet absorption and 3D-fluorescence spectroscopy. Total fluorescence was correlated (p < 0.05) with dissolved organic carbon (DOC) concentration. Fluorescence excitation-emission matrices were deconvolved using parallel factor analysis. We validated a five-component model that represented >97% of the total fluorescence. Our model includes three humic-like components, one protein-like component, and one novel component exclusively observed in highly acidic springs. The closest spectral match to the novel component is an acid-soluble lignin produced during high-temperature, acid digestion of wood pulp. Humic-like components were dominant in mid-pH springs (4 < pH < 7) indicating these springs had greater terrestrial carbon input. Acidic springs also exhibited evidence for terrestrial carbon input. Alkaline springs, in contrast, consistently had low dissolved organic carbon content and low fluorescence intensity suggesting that these springs had little terrestrial input. This absence of terrestrial carbon implies a predominantly hydrothermal fluid source. A comparison of the traditional fluorescence indices with our five model components suggest that these indices may have limited utility in continental hot springs with multiple organic matter sources and alteration processes.

Identification of antigens presented by MHC for vaccines against tuberculosis

Mycobacterium tuberculosis (M.tb) is responsible for more deaths globally than any other pathogen. The only available vaccine, bacillus Calmette-Guérin (BCG), has variable efficacy throughout the world. A more effective vaccine is urgently needed. The immune response against tuberculosis relies, at least in part, on CD4+ T cells. Protective vaccines require the induction of antigen-specific CD4+ T cells via mycobacterial peptides presented by MHC class-II in infected macrophages. In order to identify mycobacterial antigens bound to MHC, we have immunoprecipitated MHC class-I and class-II complexes from THP-1 macrophages infected with BCG, purified MHC class-I and MHC class-II peptides and analysed them by liquid chromatography tandem mass spectrometry. We have successfully identified 94 mycobacterial peptides presented by MHC-II and 43 presented by MHC-I, from 76 and 41 antigens, respectively. These antigens were found to be highly expressed in infected macrophages. Gene ontology analysis suggests most of these antigens are associated with membranes and involved in lipid biosynthesis and transport. The sequences of selected peptides were confirmed by spectral match validation and immunogenicity evaluated by IFN-gamma ELISpot against peripheral blood mononuclear cell from volunteers vaccinated with BCG, M.tb latently infected subjects or patients with tuberculosis disease. Three antigens were expressed in viral vectors, and evaluated as vaccine candidates alone or in combination in a murine aerosol M.tb challenge model. When delivered in combination, the three candidate vaccines conferred significant protection in the lungs and spleen compared with BCG alone, demonstrating proof-of-concept for this unbiased approach to identifying new candidate antigens.

Spectrum Measurement of Variable Irradiance Controlled LED-Based Solar Simulator

Efficiency of photovoltaic devices are based on testing all equipment and systems used to produce devices in a healthy way, from electrical and optical points of view. Considering that photovoltaic devices which enable the generation of electric energy from solar energy as a renewable energy source are widely used solar simulators should be utilized to provide actual daylight spectrum distributions. The most considerable component of solar simulators in photovoltaic device research is the light sources they use. Due to their advantages over conventional light sources, such as compactness, low power consumption, and low cost, research and studies on solar simulators use LEDs took place as an their illumination source. In this study, it is aimed to produce a low-cost Class A solar simulator with using six different type power LEDs, as specified in the IEC 60904-9 and ASTM E927-05 standards. At the end of the study with 24 power LEDs with six different wavelengths, the spectral match which is one of the three performance criteria of the solar simulator was obtained as Class A.

CaS:Eu2+@CaZnOS:Mn2+: A dual-UV/green-excited and dual-red-emitting spectral conversion with all-weather resistance

To pursue green ecological agriculture, long-life functionalized green house is looking for solar spectrum conversions with well spectral match, outstanding thermal and chemical stabilities, excellent anti-photobleaching and UV-conversion performance. CaS:Eu2+-based phosphors are widely known as a candidate for agricultural films with remarkable green-to-red conversion performance while they are also notorious for poor stabilities. In this work, a core-shell structured CaS:Eu2+@CaZnOS:Mn2+ composite phosphor was synthesized by a two-step solid state method. The coating layer of thermally and chemically inert oxysulfide prevents the sulfide core phosphor from hydrophilic, air-sensitive and heat-induced decompositions. Moreover, the addition of Mn2+ in CaZnOS shell qualifies the composite phosphor with strong UV absorption, which contributes to the improvement of the anti-photodegradation of the inner CaS:Eu2+ and the anti-aging of practical polymer films. The additional UV-to-orange red conversion resulting from CaZnOS:Mn2+ further extends the red components for the indoor plants. As a result, this dual UV/green excited and dual red-emitting super stable chalcogenide phosphor can be used as a general spectral conversion for plant cultivation.

Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains

Detecting and monitoring landslides are hot topics in remote sensing community, particularly with the development of remote sensing technologies and the significant progress of computer vision. To the best of our knowledge, no study focused on deep learning-based methods for landslide detection on hyperspectral images. We proposes a deep learning framework with constraints to detect landslides on hyperspectral image. The framework consists of two steps. First, a deep belief network is employed to extract the spectral–spatial features of a landslide. Second, we insert the high-level features and constraints into a logistic regression classifier for verifying the landslide. Experimental results demonstrated that the framework can achieve higher overall accuracy when compared to traditional hyperspectral image classification methods. The precision of the landslide detection on the whole image, obtained by the proposed method, can reach 97.91%, whereas the precision of the linear support vector machine, spectral information divergence, and spectral angle match are 94.36%, 84.50%, and 86.44%, respectively. Also, this article reveals that the high-level feature extraction system has a significant potential for landslide detection, especially in multi-source remote sensing.