Surface Emissivity Research Articles

Inverted optical functionality of phase change material surfaces through Mie resonances of VO2@Si core–shell structures

Vanadium dioxide (VO2) has attracted extensive attention due to its reversible transition from the insulator to metal phase at a critical temperature of 68°C. Below the critical temperature VO2 transmits the infrared radiation in the insulator phase, whereas above the critical temperature VO2 reflects the infrared portion of the incident radiation. However, smart surface interfaces for high-temperature emitter surfaces require the opposite functionality within the 1–3 µm spectral range. Here, we demonstrate that a core–shell structure, composed of VO2@Si, which is deposited on a thin layer of Ag, achieves the inverted optical functionality within the 1–3 µm spectral range, making it ideal as smart interfaces for radiative heat applications as high-temperature emitters. The proposed material architecture also increases the thermal stability of VO2 in addition to enhancing its optical properties in near-infrared region. The results were obtained using numerical simulations. Our results indicate that in its metallic state, the core–shell structure with metallic underlayer promotes efficient absorption in the near-infrared spectrum. On the other hand, in its insulating state dielectric resonances within the core–shell structure along with the metallic underlayer, resulting in increased reflection, offer inverse optical functionalities. Our findings present a significant step toward designing dynamic filters that can efficiently capture and respond to changing conditions in the near-infrared spectrum.

Machine learning-based retrieval of total column water vapor over land using GMI-sensed passive microwave measurements

ABSTRACT The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vapor (TCWV) data records from the GPM GMI sensor have been exclusively offered over oceanic regions. It is challenging to retrieve TCWV over land from satellite MW measurements because of varying land surface characteristics. In this paper, a novel Light Gradient Boosting Machine-based retrieval algorithm is proposed to derive TCWV over land from GMI-sensed MW brightness temperature (BT) observations. The GMI-observed MW BT at 18.7 GHz and 23.8 GHz, differential BT between 18.7 GHz and 23.8 GHz, latitude, longitude, and month are selected and utilized as the input variables of the retrieval approach, because of their strong correlation with satellite-sensed MW TCWV retrievals. Instead of surface emissivity data or radiative transfer model, we take into account the spatial and temporal elements, namely latitude, longitude, and month. The training of the retrieval method is performed based on ground-based TCWV estimates from worldwide 4,471 Global Navigation Satellite System (GNSS) stations in 2017. The performance of the newly proposed retrieval algorithm is independently validated in a worldwide coverage using reference TCWV from additional 4,341 GNSS stations in 2018–2020 and 605 radiosonde stations in 2017–2020. The newly retrieved TCWV estimates over land have a correlation coefficient of 0.76 and 0.83, a root-mean-square error (RMSE) of 5.82 mm and 6.02 mm, a relative RMSE of 34.91% and 34.36%, and a mean bias of 0.02 mm and −0.42 mm compared to reference TCWV from GNSS and radiosonde, respectively. The performance of the retrieval algorithm is satisfactory when compared to that of land-purpose TCWV of other satellite missions, though we have not used either surface emissivity data or radiative transfer model. This result increases confidence in retrieving TCWV over land from satellite-sensed MW BT measurements based on machine learning using ground-based TCWV observations. The newly developed retrieval algorithm has the potential for integration into operational satellite missions or meteorological services, thereby enhancing weather forecasting, climate modeling, and other relevant applications.

Comparative Study of the Effect of Volcanic Activity and the El Niño Phenomenon on Surface Thermal Changes in the Arjuno-Welirang Crater Area Using GEE Cloud Computing

The increasing seismic activity of Arjuno Welirang on February 7, 2018, warned about the dangers of volcanic eruptions that must be watched out for, so that activity needs to be monitored continuously. There is little information available about seismic activity at the Arjuno Welirang complex. Most studies have investigated geothermal potential and subsurface structures. Due to the limitations of this data, land surface temperature (LST) analysis was carried out in this research to monitor the development of Arjuno-Welirang volcanic activity. Several volcano studies have used LST criteria in remote sensing applications to detect volcanic activity. However, the performance of LST has not been tested in the case of normal volcanic activity, which is influenced by external thermal disturbances. This study aims to show the performance of LST detection on changes in the normal volcanic activity of Arjuno Welirang, which is disturbed by temperature noise caused by the El Niño phenomenon. The LST calculations are carried out by including surface emissivity corrections obtained based on Normalized Difference Vegetation Index (NDVI) values. The LST calculation algorithm is implemented with the Google Earth Engine (GEE) platform. LST analysis was carried out on Landsat 8 images for the May-August 2023 time period obtained from the United States Geological Survey (USGS) database via GEE cloud computing. The reason for determining this time period is because the release of the daily Arjuno Welirang volcanic activity report by Magma Indonesia coincided with the Meteorology Climatology and Geophysical Agency (BMKG) notification regarding the occurrence of El Niño phenomenon. The output is an LST curve against time, which shows a tendency for the surface temperature of the Arjuno-Welirang crater to increase in July. This is similar to the trend of increasing seismic activity reported on the Magma Indonesia website and conversely is not similar to the increase in temperature due to the El Niño effect predicted by BMKG. The highest temperature increase occurred on July 24 2023, reaching 31.78 ⁰C, and then the temperature decreased. The method used in this research has proven that LST analysis is able to detect changes in normal volcanic activity in the Arjuno-Welirang crater even though there is thermal noise due to the influence of the impact of El Niño. LST values are more dominantly influenced by volcanic activity than by extreme El Niño weather.

Infrared emissivity of icy surfaces

Context. Most analyses of the infrared emission of Saturn’s rings and icy satellites have considered pure water ice as the constituent of regolith and particle surfaces. Visual and near-infrared observations have shown, however, that darkening and reddening contaminants are present at a fraction level of a few percent. In the spectral domain 10–2000 cm−1, water ice becomes transparent in a few windows, which in particular causes the roll-off of emissivity of icy surfaces that is observed below 50 cm−1. Their emissivity there may be affected by these contaminants. Aims. We present a quantitative global sensitivity analysis of a hybrid Mie-Hapke model to evaluate the influence of regolith properties and contaminant fraction on the infrared emissivity of icy rings or moons over this spectral range. Methods. A hybrid Mie–Hapke model of the hemispherical emissivity ε*h(Wn) was made, including various diffraction correction and mixing types with tholins or amorphous carbon grains, or grain size distributions and some anisotropy in emission. A Sobol global sensitivity analysis provided quantitative levels of importance for these factors versus wave number wn. Results. Given the a priori uncertainties, the most important factor acting on ε*h(Wn) remains the size distribution of regolith grains and the average anisotropy factor ξ. For wn> 50 cm−1, ξ, the power-law index p and the minimum amin of the size distribution are most influential. In windows of water-ice transparency (10–50, 300–600, and 900–1300 cm−1), the emissivity is also sensitive, but to a lesser extent, to the maximum grain size amax and the fraction f of contaminants, if mixed at the molecular level. Conclusions. This model provides a self-consistent tool for interpreting multi-modal observations of the thermal emission from icy surfaces. It also offers interesting insights into recent mid-infrared observations of Saturn’s rings and Jupiter’s moon Ganymede by the JWST-MIRI instrument.

Structuring of the Surface of Thin Carbon Films During Activation by Microsecond Current Pulses

The influence of current activation by electric pulse breakdown on changes in surface morphology and emission characteristics of a field emission cathode made on the basis of carbon films obtained by deposition in a microwave gas discharge plasma was studied. Current activation of these films was carried out by applying voltage pulses of microsecond duration until an electrical breakdown occurred. It is shown that during activation, the morphology of the film surface in the breakdown region changes with the formation of a micro-sized emitting structure, which significantly improves the field emission characteristics of cathodes based on carbon films.

Recent advances in vanadium dioxide for dynamic thermal radiation modulation: A review

Thermal radiation of materials can be manipulated for the necessary applications in thermal management and infrared camouflage. To our knowledge, controlling surface emissivity is crucial for modulating thermal radiative energy, therefore, dynamic modulation of emissivity has received more attention. Vanadium dioxide (VO2) stands out as a highly promising candidate due to its capacity for reversible transition from monoclinic VO2(M) to rutile VO2(R) near room temperature. This transition results in a rapid change from transparent to opaque within the mid-infrared spectrum accompanied by a reduction in emissivity. This review offers an overview of recent progresses in the dynamic control of thermal radiation of the surface by using VO2. It also summarizes the preparation, modulation mechanisms, and applications of positive and negative differential emissivity devices. Finally, the challenges and opportunities in advancing research on VO2-based smart devices are discussed.

Influence of an inclined thick partition on free convection heat transfer performance under surface radiation in a hollow block

In the present work, a computational analysis of the combined energy transfer by free convection, radiation, and heat conduction in 2D hollow block with an inclined partition was carried out. The inclined partition was made of a heat-conducting material and had a finite thickness. The closed space inside the brick was filled with air (Pr = 0.71) and the airflow is laminar. The external surfaces of the vertical borders are considered to be isothermal, and the remaining horizontal surfaces are supposed to be adiabatic. The finite difference technique is employed to work out numerically the differential equations. The in-house computational code was verified in detail using various problems. The major characteristics that determine the considered phenomena are surface emissivity of internal walls, Ra number, and material of solid walls and partition. As a result of the research, the distributions of streamlines and isotherms combined with the average Nusselt numbers were obtained. Depending on the material of the partition, the flow structure in the cavity changes significantly, which reflects the distribution of streamlines, namely, for the materials with low thermal conductivity the convective cell in the upper part of the cavity is elongated, while in the lower part it is shifted to the lower and left borders. The isotherms reflect the more intensive heating in the left part of the area. With an increase in the thermal conductivity coefficient, the streamlines reflecting the nature of the flow are restructured. It was shown that the presence of an inclined partition significantly reduces the intensity of convective heat transfer.

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

ABSTRACT The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Monte Carlo method for evaluation of surface emission rate measurement uncertainty

Monte Carlo method for evaluation of surface emission rate measurement uncertainty

Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting

AbstractSatellite‐observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud, and precipitation is inferred directly using all‐sky radiance data assimilation. In contrast, information on the surface state, such as sea‐surface temperature (SST) and sea‐ice concentration (SIC), is typically provided through third‐party retrieval products. Scientifically, this is a sub‐optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 h. A better solution is to estimate the surface and atmospheric state jointly from the radiance observations. This has not been possible until now, due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and an empirical sea‐ice surface emissivity model is used here to add sea‐ice state estimation, including SIC, to the European Centre for Medium‐range Weather Forecasts atmospheric data assimilation system. The sea‐ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea‐ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean–atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.

Simultaneous Inference of Sea Ice State and Surface Emissivity Model Using Machine Learning and Data Assimilation

AbstractSatellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly‐known microstructural properties that vary strongly in time and space. A consequence is that surface‐sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical‐empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.

Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

Fuzzy logic based thermal image processing for temperature monitoring of rotating cylindrical surfaces

When a non-contact and remote temperature measurement is of importance, several environmental factors must be considered. Of these, two are of crucial importance — the surface emissivity of the observed object and the directional emissivity in the object–sensor relationship. This paper proposes FDEM — a Fuzzy Directional Emissivity Model built with the assumption that the observed surface is cylindrically shaped. The model consists of two submodels — for vertical and horizontal directions separately. The main contribution of this paper is the formalization of a linguistic description of measurement conditions typical for an industrial environment. Additionally, dual linguistic fuzzy modifiers were used to increase the accuracy of the modeled emissivity. Finally, the proposed model achieved up to 40% reduction of RMSE error when used for correcting the temperature measurements of a rotating cylinder. Overall, our model provides a novel and reliable way for temperature readout correction on the entire surface of a drying roller, such as ones used in the papermaking industry. Furthermore, the proposed approach is not only inherently capable of compensating for the position of the camera, but also does it in an interpretable, and hence expert-verifiable, manner.

Development and characterization of plasma electrolytic oxidation containing hydroxyapatite coating on selective laser melted Ti6Al4V porous scaffold

Ti6Al4V porous scaffolds are designed and fabricated to solve the elastic modulus mismatch between bone and an implant. Bioactive coating is applied on the surface of the implant to improve biocompatibility of titanium implants and form chemical bonds with surrounding bone. Therefore, this study aims to develop a bio-functional coating on Selective Laser Melting (SLM) manufactured porous Ti6Al4V scaffold produced using plasma electrolytic oxidation technique (PEO). Hydroxyapatite (HAp) and fluorapatite (FAp) were formed on the scaffold surface and field emission scanning electron microscopy (FESEM) was used to study the surface morphology and thickness of the coating. The chemical composition of the coatings was investigated by using an energy dispersive spectroscopy (EDS), X-ray diffractometer (XRD), and X-ray photoelectron spectroscopy (XPS). The coating formation was non-homogeneous with Ca/P ratio range of 1.24 to 1.41, approaching ratio of tricalcium phosphate compound. In vitro bioactivity test using a simulated body fluid (SBF) was conducted and the apatite formation on the coating was analyzed. PEO coatings on Ti6Al4V SLM-manufactured porous scaffolds containing fluoride and calcium salt have shown good bioactivity with the apatite formation that can be observed on all coated scaffolds. It was concluded that the combination of bio-functional coating on the surface of the SLM-manufactured porous scaffold provides synergistic effects, which was beneficial in improving the biocompatibility of orthopedic porous metallic implants. Hence, SLM-PEO approach potentially can be adapted to develop a bioactive titanium implant with tunable mechanical properties for orthopedic applications.

Temperature distribution in stretching/shrinking fin with variable parameters

AbstractIn this paper, we consider a mathematical model, which has a unique mechanism of heat transfer in the stretching/shrinking straight fin with an exponential profile. The thermal conductivity, internal heat generation, and heat transfer coefficient are considered temperature‐dependent. Heat is exposed to the surroundings by convection and radiation. The governing differential equation and boundary conditions are presented in a dimensionless form. In our study, we considered variable surface emissivity, that is, a constant, and the linear function of a temperature. The convective heat transfer parameter is considered a power‐low type. The novelty of this work is the application of temperature‐dependent surface emissivity, and the problem is solved by the Legendre wavelet collocation method. A comparative analysis of the present results in the context of previous findings is presented in the form of a table for validation and found exactly the same. The impacts of distinct variables are presented in the form of figures and discussed in detail. The present analysis is focused on real‐world applications and offers valuable insights for improving the design of fins.

Resonator embedded photonic crystal surface emitting lasers

The finite size of 2D photonic crystals results in them being a lossy resonator, with the normally emitting modes of conventional photonic crystal surface emitting lasers (PCSELs) differing in photon lifetime via their different radiative rates, and the different in-plane losses of higher order spatial modes. As a consequence, the fundamental spatial mode (lowest in-plane loss) with lowest out-of-plane scattering is the primary lasing mode. For electrically driven PCSELs, as current is increased, incomplete gain clamping results in additional spatial (and spectral) modes leading to a reduction in beam quality. A number of approaches have been discussed to enhance the area (power) scalability of epitaxy regrown PCSELs through careful design of the photonic crystal atom1–3. None of these approaches tackle the inflexibility in being unable to independently modify the photon lifetime of the different modes at the Γ2 point. As a method to introduce design flexibility, resonator embedded photonic crystal surface emitting lasers (REPCSELs) are introduced. This device, combining comparatively low coupling strength photonic crystal structures along with perimeter mirrors, allow a Fabry–Pérot resonance effect to be realised that provides wavelength selective modification of the photon lifetime. We show that surface emission of different surface emitting modes may be selectively enhanced, effectively changing the character of the modes at the Γ2 point. This is a consequence of the selective modification of in-plane loss for particular modes, and is dependent upon the alignment of the photonic crystal (PhC) band-structure and distributed Bragg reflectors’ (DBRs) reflectance spectrum. These findings offer new avenues in surface emitting laser diode engineering. The use of DBRs to reduce the lateral size of a PCSEL opens the route to small, low threshold current (Ith), high output efficiency epitaxy regrown PCSELs for high-speed communication and power sensitive sensing applications.

Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Water Soil Availability Prediction in Coffee Land Plantations with Remote Sensing

Abstract The availability of soil water is the basis of plant growth, so its availability is important key for coffee plants growing in South Malang. Climate change makes the availability of water for these plants a complex problem because changing temperature and humidity. Remote sensing is a fast and cheap way to predict water availability in coffee plantations with large land areas and rapid climate change. The aims of this research to estimate water soil availability in coffee land plantations to meet plant water needs. The water content in coffee plantations can be estimated by combining the vegetation index values with a combination of Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), Land Surface Temperature (LST), and Land Surface Emissivity (LSE). The method used in this research is to make tentative maps obtained from overlays of all combining the vegetation index values. The selection of research locations is based on the area of the coffee plantation using the smallest pixel, with an area of 3.000.000 m square meters. Each location of the research location was carried out four times for sampling the soil moisture content. The results showed that the correlation value between water content and the estimation results had a correlation of 0.814, with a regression value of 0.663 and a probability value of 0.9226. This shows that it is possible to estimate the water content of coffee plantations using remote sensing made it easier for coffee farmers to take action based on actual conditions on the land.

Facile synthesis of CuOx-decorated TiO2 nanoparticles via oxygen-limited pyrolysis of Cu(II) complex for efficient photocatalytic hydrogen evolution

CuOx-decorated TiO2 (CuOx/TiO2) nanoparticles have been prepared by the pyrolysis of Cu(II) complex on TiO2 in an oxygen-limited atmosphere. A solution phase deposition method combined with freeze drying was employed for depositing the 2-methylimidazole-Cu(II) complex onto TiO2 nanoparticles. The effect of decoration of CuOx on TiO2 nanoparticles in terms of the crystal phase, morphology, chemical compositions, surface area, surface elemental states, optical absorption and emission properties, was investigated using a series of characterization tools. The oxygen-limited pyrolysis of 2-methylimidazole resulted in the reduction of partial Ti4+ and Cu2+ ions into Ti3+, Cu+ and Cu0 species, respectively. The surface decoration of CuOx was found to improve the separation and migration of photogenerated electron-hole pairs and increase the life time of excitons. Furthermore, the photocatalytic performance of CuOx/TiO2 nanoparticles was evaluated for hydrogen evolution in aqueous methanol solution under UV–visible light irradiation. The CuOx/TiO2 photocatalyst with the optimal cocatalyst loading achieved hydrogen evolution rate of 4.34 mmol h−1 g−1, with 71- and 5.5-fold enhancement in activity over neat TiO2 and CuOx/P25, respectively. It is expected that the combination of deposition and pyrolysis of 2-methylimidazole-Cu(II) complex could be an effective approach for the decoration of Cu and its oxides on other supports.

Infrared Spectral Emissivity Dynamics of Surfaces Under Water Condensation

AbstractWater condensation on a surface strongly affects its effective emissivity, especially in the atmospheric window, a wavelength range essential for outdoor applications related to energetically passive cooling and heating. The evolution of emissivity of a silicon surface during dropwise and filmwise water deposition is studied. The evolution of the spectral radiative properties shows that the increase in effective emissivity due to the growth of a droplet pattern is steeper than for a growing water film of equivalent thickness. The change of surface emissivity takes place in the first moments of condensation where droplets as small as 10 µm drastically impact the reflectance of the pristine surface. The upper limit of effective emissivity is reached for a droplet radius or film thickness of 50 µm. During dropwise condensation, effective emissivity is weighted by the drop surface coverage and then remains within an asymptotic maximum value of 0.8, while in case of filmwise condensation, it is shown to reach 0.9 corresponding to water emissivity. Micrometer‐scale spatially‐resolved infrared spectral images enable to correlate the spatial variation of spectral properties to the droplet size and localization. Such findings are of interest to the implementation of moisture‐controlled emissivity tuning and radiative sky‐coolers for dew harvesting.