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Luminous efficiency is a pivotal factor for assessing the performance of optoelectronic devices, wherein light loss caused by diverse factors is harvested and converted into the radiative mode. In this study, we demonstrate a nanoscale vacuum photonic crystal layer (nVPCL) for light extraction enhancement. A corrugated semi-transparent electrode incorporating a periodic hollow-structure array was designed through a simulation that utilizes finite-difference time-domain computational analysis. The corrugated profile, stemming from the periodic hollow structure, was fabricated using laser interference lithography, which allows the precise engineering of various geometrical parameters by controlling the process conditions. The semi-transparent electrode consisted of a 15 nm thick Ag film, which acted as the exit mirror and induced microcavity resonance. When applied to a conventional green organic light-emitting diode (OLED) structure, the optimized nVPCL-integrated device demonstrated a 21.5% enhancement in external quantum efficiency compared to the reference device. Further, the full width at half maximum exhibited a 27.5% reduction compared to that of the reference device, demonstrating improved color purity. This study presents a novel approach by applying a hybrid thin film electrode design to optoelectronic devices to enhance optical efficiency and color purity. [ABSTRACT FROM AUTHOR]

Zhao et al. [[11]] used the daily MODIS cloud-free snow cover product to calculate various snow phenology variables, including snow cover area (SCA), snow cover onset (SCS), snow cover melt (SCM) and snow cover day (SCD). This Special Issue contains many research highlights related to the remote sensing of mountain snow cover and vegetation and the relationship between vegetation and snow. Climate change has profoundly impacted elements of land surface at high latitudes and elevations, especially snow and vegetation. The time of snow accumulation and melting (snow phenology) is closely related to the vegetation growth condition. [Extracted from the article]

Thiodiglycolic acid (TDGA) is a biomarker for monitoring vinyl chloride exposure. Exploring a facile, rapid and precise analysis technology to quantify TDGA is of great significance. In this research, we demonstrate a fluorescent sensor based on dual-emissive UiO-66 for TDGA detection. This ratiometric fluorescent material named C460@Tb-UiO-66-(COOH)2 was designed and synthesized by introducing organic dye 7-diethylamino-4-methylcoumarin (C460) and Tb3+ into UiO-66-(COOH)2. The as-obtained C460@Tb-UiO-66-(COOH)2 samples showed highly selective recognition, excellent anti-interference and rapid response characteristics for the recognition of TDGA. The detection limit is 0.518 mg·mL−1, which is much lower than the threshold of 20 mg·mL−1 for a healthy person. In addition, the mechanism of TDGA-induced fluorescence quenching is discussed in detail. This sensor is expected to detect TDGA content in human urine. [ABSTRACT FROM AUTHOR]

As an important biomarker in urine, the level of uric acid is of importance for human health. In this work, a Cu(II) functionalized metal–organic framework (Cu2+@Tb-MOFs) is designed and developed as a novel fluorescence probe for wide-range uric acid detection in human urine. The study shows that this fluorescence platform demonstrated excellent pH-independent stability, high water tolerance, and good thermal stability. Based on the strong interaction between metal ions and uric acid, the designed Cu2+@Tb-MOFs can be employed as efficient turn-on fluorescent probes for the detection of uric acid with wide detection range (0~104 µM) and high sensitivity (LOD = 0.65 µM). This probe also demonstrates an anti-interference property, as other species coexisted, and the possibility for recycling. The sensing mechanisms are further discussed at length. More importantly, we experimentally constructed a molecular logic gate operation based on this fluorescence probe for intelligent detection of uric acid. These results suggest the Cu(II) functionalized metal–organic framework can act as a prominent candidate for personalized monitoring of the concentration of uric acid in the human urine system. [ABSTRACT FROM AUTHOR]

Developing highly efficient and recyclable catalysts for the transformation of toxic organic contaminates still remains a challenge. Herein, Titanium Carbide (Ti3C2) MXene modified by alkali treatment process was selected as a support (designated as alk-Ti3C2X2, where X represents the surface terminations) for the synthesis of Pd/alk-Ti3C2X2. Results show that the alkali treatment leads to the increase of surface area and surface oxygen-containing groups of Ti3C2X2, thereby facilitating the dispersion and stabilization of Pd species on the surface of alk-Ti3C2X2. The Pd/alk-Ti3C2X2 catalyst shows excellent catalytic activity for the hydrodechlorination of 4-chlorophenol and the hydrogenation of 4-nitrophenol in aqueous solution at 25 °C and hydrogen balloon pressure. High initial reaction rates of 216.6 and 126.3 min-1·g-1pd are observed for the hydrodechlorination of 4-chlorophenol and hydrogenation of 4-nitrophenol, respectively. Most importantly, Pd/alk-Ti3C2X2 exhibits excellent stability and recyclability in both reactions without any promoters. The superior property of Pd/alk-Ti3C2X2 makes it as a potential material for practical applications. [ABSTRACT FROM AUTHOR]

Forest canopy height is an important biophysical variable for quantifying carbon storage in terrestrial ecosystems. Active light detection and ranging (lidar) sensors with discrete-return or waveform lidar have produced reliable measures of forest canopy height. However, rigorous procedures are required for an accurate estimation, especially when using waveform lidar, since backscattered signals are likely distorted by topographic conditions within the footprint. Based on extracted waveform parameters, we explore how well a physical slope correction approach performs across different footprint sizes and study sites. The data are derived from airborne (Laser Vegetation Imaging Sensor; LVIS) and spaceborne (Geoscience Laser Altimeter System; GLAS) lidar campaigns. Comparisons against field measurements show that LVIS data can satisfactorily provide a proxy for maximum forest canopy heights (n = 705, RMSE = 4.99 m, and R2 = 0.78), and the simple slope correction grants slight accuracy advancement in the LVIS canopy height retrieval (RMSE of 0.39 m improved). In the same vein of the LVIS with relatively smaller footprint size (~20 m), substantial progress resulted from the physically-based correction for the GLAS (footprint size = ~50 m). When compared against reference LVIS data, RMSE and R2 for the GLAS metrics (n = 527) are improved from 12.74-7.83 m and from 0.54-0.63, respectively. RMSE of 5.32 m and R2 of 0.80 are finally achieved without 38 outliers (n = 489). From this study, we found that both LVIS and GLAS lidar campaigns could be benefited from the physical correction approach, and the magnitude of accuracy improvement was determined by footprint size and terrain slope. [ABSTRACT FROM AUTHOR]

Arctic-Boreal region-mainly consisting of tundra, shrub lands, and boreal forests-has been experiencing an amplified warming over the past 30 years. As the main driving force of vegetation growth in the north, temperature exhibits tight coupling with the Normalized Difference Vegetation Index (NDVI)-a proxy to photosynthetic activity. However, the comparison between North America (NA) and northern Eurasia (EA) shows a weakened spatial dependency of vegetation growth on temperature changes in NA during the past decade. If this relationship holds over time, it suggests a 2/3 decrease in vegetation growth under the same rate of warming in NA, while the vegetation response in EA stays the same. This divergence accompanies a circumpolar widespread greening trend, but 20 times more browning in the Boreal NA compared to EA, and comparative greening and browning trends in the Arctic. These observed spatial patterns of NDVI are consistent with the temperature record, except in the Arctic NA, where vegetation exhibits a similar long-term trend of greening to EA under less warming. This unusual growth pattern in Arctic NA could be due to a lack of precipitation velocity compared to the temperature velocity, when taking velocity as a measure of northward migration of climatic conditions [ABSTRACT FROM AUTHOR]

The dynamics of terrestrial vegetation have changed a lot due to climate change and direct human interference. Monitoring these changes and understanding the mechanisms driving them are important for better understanding and projecting the Earth system. Here, we assessed the dynamics of vegetation in a semi-arid region of Northwest China for the years from 2000 to 2019 through satellite remote sensing using Vegetation Index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and analyzed the interannual covariation between vegetation and three climatic factors—air temperature, precipitation, and vapor pressure deficit (VPD)—at nine meteorological stations. The main findings of this research are: (1) herbaceous land greened up much more than forests (2.85%/year vs. 1.26%/year) in this semi-arid region; (2) the magnitudes of green-up for croplands and grasslands were very similar, suggesting that agricultural practices, such as fertilization and irrigation, might have contributed little to vegetation green-up in this semi-arid region; and (3) the interannual dynamics of vegetation at high altitudes in this region correlate little with temperature, precipitation, or VPD, suggesting that factors other than temperature and moisture control the interannual vegetation dynamics there. [ABSTRACT FROM AUTHOR]

Demographics and psychographics are used to study the influence of different consumers on product effects in food development and testing. Demographics have a longer history and are routinely used in most research; psychographics are more recent, raising the question of whether they add to research on food products. The research presented here represents extensive exploratory data that demonstrate that both demographic measures and psychographic measures add to our understanding of consumer's liking ratings for nutrient supplements. The results are discussed in the context of broader research on a range of food products. In addition, the research reported here was conducted in four different countries, demonstrating many country effects. Finally, tests were conducted with users of the products, lapsed users of the product, and users of other nutrient supplements (non-users), and this led to many differences in product testing. These results further suggest that age and gender are not the only demographic variables to be studied, along with psychographic variables. The psychographic variables should be selected for a particular product category under investigation, as effects of specific psychographic measures vary for product categories. Specific variables do not fit all products for both demographics and psychographics. [ABSTRACT FROM AUTHOR]

We evaluated the performance of three global evapotranspiration (ET) models at local, regional, and global scales using the multiple sets of leaf area index (LAI) and meteorological data from 1982 to 2017 and investigated the uncertainty in ET simulations from the model structure and forcing data. The three ET models were the Simple Terrestrial Hydrosphere model (SiTH) developed by our team, the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), and the MODerate Resolution Imaging Spectroradiometer (MODIS) ET algorithm (MOD16). Comparing the observed with simulated monthly ET by the three models over 43 Fluxnet sites, we found that SiTH overestimated ET for forests with mean slope from 1.25 to 1.67, but it performed better than the other two models over short vegetation. MOD16 and PT-JPL models simulated well for forests but poorly in dryland biomes (slope = 0.25~0.55; R2 = 0.02~0.46). At the catchment scale, all models performed well, except for some tropical and high latitudinal catchments, with NSE values lower than 0 and RMSE and MAE values far beyond their mean values. At the global scale, SiTH highly overestimated ET in tropics, while PT-JPL slightly underestimated ET between 30°N and 60°N and MOD16 underestimated ET between 15°S and 30°S. Generally, the PT-JPL provided the better performance than SiTH and MOD16 models. This study also revealed that the estimated ET by SiTH and especially PT-JPL model were influenced by the uncertainty in meteorological data, and the estimated ET was performed better using MERRA-2 datasets for PT-JPL and using ERA5 datasets for SiTH. While the estimated ET by MOD16 were relatively sensitive to LAI data. In addition, our results suggested that the GLOBMAP and GIMMS datasets were more suitable for long-term ET simulations than the GLASS dataset. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time. [ABSTRACT FROM AUTHOR]

Conventional drivers of liking analysis was extended with a time dimension into temporal drivers of liking (TDOL) based on functional data analysis methodology and non-additive models for multiple-attribute time-intensity (MATI) data. The non-additive models, which consider both direct effects and interaction effects of attributes to consumer overall liking, include Choquet integral and fuzzy measure in the multi-criteria decision-making, and linear regression based on variance decomposition. Dynamics of TDOL, i.e., the derivatives of the relative importance functional curves were also explored. Well-established R packages ‘fda’, ‘kappalab’ and ‘relaimpo’ were used in the paper for developing TDOL. Applied use of these methods shows that the relative importance of MATI curves offers insights for understanding the temporal aspects of consumer liking for fruit chews. [ABSTRACT FROM AUTHOR]