Global Spectrum Research Articles

Layered target design method for global spectrum optimization of radioisotope production

Targets are irradiated in high-flux reactors to produce transplutonium isotopes. Neutron environment of the target is crucial for the production efficiency of transplutonium isotopes. To improve the production efficiency of transplutonium isotopes, it is necessary to research the optimization design of target. Taking the production of Californium-252 as an example, this study analyzed the impact of self-shielding effect in targets on the yield of transplutonium isotope based on the High Flux Isotope Reactor (HFIR) and High-Flux Fast Reactor (HFFR). The self-shielding effect leads to the hardening of the neutron spectrum inside the target and significantly reduces the conversion rate of nuclides. After conducting a refined energy spectrum analysis, we proposed a layered target design method based on the Genetic Algorithm (GA). To reduce computational costs, we propose a fixed source-burnup coupling approximate calculation method, which can avoid tedious burnup calculation and provide optimization direction. Using this method, we designed an optimal layered target scheme. Compared with non-layered target, the production efficiency of Cf-252 was increased by approximately 4.1 times. This study provides technical support for energy spectrum analysis and target design in producing transplutonium isotopes.

Global spectrum model of discrete dislocation equation

The fully discrete dislocation equation can be rationally derived from the spectrum model. In this paper, the previous spectrum model is reinterpreted to cover the global feature of the spectrum. Instead of the local behavior around the origin of the Brilouin zone, the global spectrum model focuses the maximum at the boundary of the Brillouin as well as the slope at the origin of the Brilouin zone. The strength of the point-contact interaction that effectively describes the discreteness effect becomes larger because the whole lattice effect is included in the global spectrum model. The validness and signification of the new model are illustrated by a solvable lattice dynamical model of the cubic lattice.

Evaluating Photovoltaic Conversion Performance under Artificial Indoor Lighting

Several photovoltaic technologies, based on different semiconductor absorbers with band-gap energy in the range Eg = 1.0–1.5 eV are currently sharing the market for outdoor applications. These photovoltaic cells are designed to achieve an optimal photovoltaic conversion under solar illumination (represented by the standard AM1.5 global spectrum), but their performance changes under different artificial indoor lights. Here, the detailed balance principle that was first applied for an ideal photovoltaic absorber under solar radiation is now used by considering the actual spectra of four typical indoor lamps: incandescent, halogen, metal halide and white LED. For each particular illumination source, the theoretical maximum for short-circuit current, open-circuit voltage and maximum power point have been calculated and represented as a function of the absorber band-gap energy. Furthermore, the optical absorption spectra of some semiconductors with optimal solar conversion efficiencies are used to estimate their comparative performance under the various artificial light sources. It has been found that wide band-gap absorbers (Eg~1.9 eV) are needed to achieve a light-to-electricity conversion efficiency of 60% under LED illumination or 31% with metal halide bulbs, while a lowest band-gap energy of about 0.8 eV is required to obtain a maximum efficiency of 24% with incandescent and halogen lamps.

DFT-based computational analysis of 2,4-dimethyl-6-tert-butylphenol (DTBP) as an antioxidant in aviation fuels

Antioxidants are indispensable additives for ensuring the long-term stability, performance, and reliability of aviation fuels. They prevent the formation of gum and other insoluble deposits in the fuel which clog fuel systems, leading to engine problems. This is achieved by inhibiting free radical chain reactions involved in the oxidation of hydrocarbons in the fuel. The compound 2,4-Dimethyl-6-tert-butyl phenol (DTBP) is one of the widely used antioxidants in aviation fuels. In this report, the radical scavenging done by DTBP is analysed computationally by density functional theory (DFT) with the model chemistry X/6-31+G(d,p), where X represents certain Minnesota functionals such as MN15, M05-2X, M06-2X, M08-HX, and M11. Among the well-known modes of radical scavenging, namely HAT, SETPT, and SPLET, the HAT mode is preferred in the gaseous phase, while SPLET operates in non-polar solvents, as computations indicate. Fukui indices, global reactivity descriptors, electronic spectra, and natural bond orbitals (NBOs) are also generated to explain the results. Understanding the mechanism of radical scavenging is important in designing powerful and efficient antioxidants for aviation fuels such as jet fuel, aviation gasoline, Jet B, and bio-kerosene.

Exploring effects of reactant’s chemical environments on adsorption and reaction mechanism by global optimization and IR spectrum calculation, using NO oxidation as a case

Reactant’s chemical environments derived from the type and concentration of surface species on heterogeneous catalysts do great effects on the structures of reactants and reaction pathway. Unraveling the arrangement of surface species and reaction mechanism at different reactant’s chemical environments in theory remains a substantial challenge due to the difficulty in obtaining the global stable configuration of surface species on catalysts and the lack of other theoretical evidences for the identification of surface species and reaction pathway. To solve this problem, a protocol combining the global optimization of surface species at different chemical environments with electronic structure analysis and IR spectrum calculation including frequency and intensity was proposed to unravel the arrangement of surface species and their reaction pathway in this work. Using NO adsorption and oxidation on Pt(111) and O-covered Pt(111) as a case, their stable structures were obtained by the global optimization; then the origins of the structural transformation of NO and NO2 at different environments were elucidated by electronic structure analysis; then the structures and reaction pathway were confirmed by comparing the experimental and calculated IR spectra of NO and NO2 with frequency and intensity. Utilizing this protocol, the stable structures of NO and its transformation, as well as oxidation pathway were revealed under varying chemical environments at the molecular level. It provides solid supports for identifying the stable arrangement of surface species, their transformation and reaction pathway, and can be easily extended to heterogeneous catalysis.

Disparities in 36 cancers across 185 countries: secondary analysis of global cancer statistics.

Cancer is a major public health problem and represents substantial disparities worldwide. This study reported estimates for 36 cancers across 185 countries by incidence, mortality, 5-year prevalence, mortality-to-prevalence ratio (MPR), and mortality-to-incidence ratio (MIR) to examine its association with human development index (HDI) and gross national income (GNI). Data were collected from the GLOBOCAN 2020. MPR and MIR were calculated by sex, age group, country, and cancer type and then summarized into totals. Segi's population and global cancer spectrum were used to calculate age- and type-standardized ratios. Correlation analyses were conducted to assess associations. Results showed that breast cancer was the most diagnosed cancer globally. Low- and middle-income countries had high MPR and MIR. Cancers of esophagus, pancreas, and liver had the highest ratios. Males and the older population had the highest ratios. HDI and GNI were positively correlated with incidence and mortality but negatively correlated with MPR/MIR. Substantial disparities in cancer burden were observed among 36 cancer types across 185 countries. Socioeconomic development may contribute to narrowing these disparities, and tailored strategies are crucial for regional- and country-specific cancer control.

Utilizing molecular states of carbon quantum dots (CQDs) to efficiently harvest outdoor and indoor energy via luminescent solar concentrator

Luminescent solar concentrators (LSCs) offer a huge potential for electricity generation due to their ability to harvest direct and diffused light. Among numerous fluorophores for LSCs, carbon quantum dots (C-QDs) are promising candidates due to their non-toxic nature, tunable optical features, and cost-effective synthesis. State-of-the-art LSCs utilizing C-QDs focus on solar energy harvesting, while their potential to harness indoor light for electricity generation is yet to be explored. In this study, we rationally fabricated C-QDs based LSC for efficient energy harvesting under both outdoor and indoor illuminations. Through a facile solvothermal pyrolysis technique, we synthesized the molecular states assisted C-QDs exhibiting a strong absorbance in the visible region that matched well with the outdoor and indoor light spectra. Laminated LSCs were fabricated by coating C-QDs/polyvinyl alcohol (PVA) film (photoluminescence quantum yield 71 %) on two glass substrates and joining them with an interlayer of refractive index matching polymer. Given geometry not only protects C-QDs/PVA film from external damage but also prevents light scattering losses that were prominent in an open C-QDs/PVA layer. External efficiency (ηext) of the small-to-large area LSCs under different illuminations were estimated using an analytical approach. The results showed that under outdoor (air mass 1.5 global spectrum) illumination and without scattering background, a large-area (10 × 10 × 0.6 cm3) LSC at an optimized concentration of C-QDs exhibited ηext of 3.1 %. When connected with the silicon PV cell, the same LSC yielded a power conversion efficiency (ηPCE) of 0.34 %. Among various indoor illuminations (light-emitting diode (LED)-daylight, LED-warm white, and fluorescent-daylight (CFL)), the best performance was shown under LED-daylight with the ηext and ηPCE of 7.4 % and 0.30 %, respectively. This study offers enormous potential for the adoption of C-QDs based LSC not only in building exteriors (such as windows and facades) but also in the places where artificial lights are used.

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.

Upright Pyramid Surface Textures for Light Trapping and MoOx Layer in Ultrathin Crystalline Silicon Solar Cells

In this work, ray tracing is used to investigate the optical characteristics of various surface structures in ultrathin crystalline silicon (c-Si) for solar cells. Ultrathin c-Si with a thickness of 20 μm is used as the substrate. The light trapping includes front upright pyramids with a molybdenum oxides (MoOx) anti-reflection (AR) layer. Planar ultrathin c-Si (without a MoOx AR layer and upright pyramids) is used as a reference. The wafer ray tracer was developed by a photovoltaic (PV) lighthouse to model the MoOx AR layer to reduce the front surface reflectance and impacts of the AR layer on ultrathin Si solar cells. The optical properties are calculated on the AM1.5 global solar energy spectrum across the 200–1200 nm wavelength region. From the absorbance profile, the photogenerated current density (Jph) in the substrate is also calculated with various surface structures. The front upright pyramids with the MoOx layer result in the largest absorbance enhancement due to the enhanced light scattering by the pyramids and MoOx AR layer. The Jph of 37.41 mA/cm2 is improved when compared to the planar ultrathin c-Si reference. This study is significant as it illustrates the potential of ultrathin c-Si as a promising PV module technology in the future.

Effects of feedback-free starburst galaxies on the 21-cm signal and reionization history

ABSTRACT Different star formation models at Cosmic Dawn produce detectable signatures in the observables of upcoming 21-cm experiments. In this work, we consider the physical scenario of feedback-free starbursts (FFB), according to which the star formation efficiency (SFE) is enhanced in sufficiently massive haloes at early enough times, thus explaining the indication from the JWST for an excess of bright galaxies at $z \ge 10$. We model the contribution of FFBs to popII SFE and compute the impact these have on the 21-cm global signal and power spectrum. We show that FFBs affect the evolution of the brightness temperature and the 21-cm power spectrum, but they only have a limited effect on the neutral hydrogen fraction. We investigate how the observables are affected by changes in the underlying star formation model and by contribution from popIII stars. Finally, we forecast the capability of next-generation Hydrogen Epoch of Reionization Array (HERA) to detect the existence of FFB galaxies via power spectrum measurements. Our results show the possibility of a significant detection, provided that popII stars are the main drivers of lowering the spin temperature. Efficient popIII star formation will make the detection more challenging.

Calibration Error in 21-Centimeter Global Spectrum Experiment

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionization. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature and how typical errors impact the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical vector network analyzer (VNA) measurement error in the magnitude of the reflection coefficients of the antenna, receiver, and open cable, which are 0.001, 0.001, and 0.002 (linear), respectively, would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48°, 0.78°, or 0.15° in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal.

Tackling the Challenges in the 21 cm Global Spectrum Experiment: The Impact of Ionosphere and Beam Distortion

The H i 21 cm global signal from the Cosmic Dawn and the Epoch of Reionization (EoR) offers critical insights into the evolution of our Universe. Yet, its detection presents significant challenges, due to its extremely low signal-to-contamination ratio and complex instrumental systematics. In this paper, we examine the effects of the ionosphere and antenna beam on data analysis. The ionosphere, an ionized plasma layer in the Earth’s atmosphere, refracts, absorbs, and emits radio waves in the relevant frequency range. This interaction results in additional spectral distortion of the observed signal, complicating the process of foreground subtraction. Additionally, chromatic variations in the beam can also introduce further contamination into the global spectrum measurement. Notably, the ionospheric effect, being dependent on the direction of incoming light, interacts with the instrumental beam, adding another layer of complexity. To address this, we evaluate three different fitting templates of foreground: the logarithmic polynomial, the physically motivated Experiment to Detect the Global EoR Signature (EDGES) template, and a singular value decomposition (SVD)-based template. Our findings indicate that the EDGES and SVD templates generally surpass logarithmic polynomials in performance. Recognizing the significance of beam chromaticity, we further investigate specific beam distortion models and their impacts on the signal extraction process.

Global Survey of Energetic Electron Precipitation at Low Earth Orbit Observed by ELFIN

AbstractWe statistically evaluate the global distribution and energy spectrum of electron precipitation at low‐Earth‐orbit, using unprecedented pitch‐angle and energy resolved data from the Electron Losses and Fields INvestigation CubeSats. Our statistical results indicate that during active conditions, the ∼63 keV electron precipitation ratio peaks at L > 6 at midnight, whereas the spatial distribution of precipitating energy flux peaks between the dawn and noon sectors. ∼1 MeV electron precipitation ratio peaks near midnight at L > ∼6 but is enhanced near dusk during active times. The energy spectrum of the precipitation ratio shows reversal points indicating energy dispersion as a function of L shell in both the slot region and at L > ∼6, consistent with hiss‐driven precipitation and current sheet scattering, respectively. Our findings provide accurate quantification of electron precipitation at various energies in a broad region of the Earth's magnetosphere, which is critical for magnetosphere‐ionosphere coupling.

Variations and trade-offs in leaf and culm functional traits among 77 woody bamboo species

BackgroundWoody bamboos are the only diverse large perennial grasses in mesic-wet forests and are widely distributed in the understory and canopy. The functional trait variations and trade-offs in this taxon remain unclear due to woody bamboo syndromes (represented by lignified culm of composed internodes and nodes). Here, we examined the effects of heritable legacy and occurrence site climates on functional trait variations in leaf and culm across 77 woody bamboo species in a common garden. We explored the trade-offs among leaf functional traits, the connection between leaf nitrogen (N), phosphorus (P) concentrations and functional niche traits, and the correlation of functional traits between leaves and culms.ResultsThe Bayesian mixed models reveal that the combined effects of heritable legacy (phylogenetic distances and other evolutionary processes) and occurrence site climates accounted for 55.10–90.89% of the total variation among species for each studied trait. The standardized major axis analysis identified trade-offs among leaf functional traits in woody bamboo consistent with the global leaf economics spectrum; however, compared to non-bamboo species, the woody bamboo exhibited lower leaf mass per area but higher N, P concentrations and assimilation, dark respiration rates. The canonical correlation analysis demonstrated a positive correlation (ρ = 0.57, P-value < 0.001) between leaf N, P concentrations and morphophysiology traits. The phylogenetic principal components and trait network analyses indicated that leaf and culm traits were clustered separately, with leaf assimilation and respiration rates associated with culm ground diameter.ConclusionOur study confirms the applicability of the leaf economics spectrum and the biogeochemical niche in woody bamboo taxa, improves the understanding of woody bamboo leaf and culm functional trait variations and trade-offs, and broadens the taxonomic units considered in plant functional trait studies, which contributes to our comprehensive understanding of terrestrial forest ecosystems.

Innovative interpretable AI-guided water quality evaluation with risk adversarial analysis in river streams considering spatial-temporal effects

Water security remains a critical issue given the looming threats of industrial pollution, necessitating comprehensive assessments of water quality to address seasonal fluctuations and influential factors while formulating effective strategies for decision makers. This study introduces a novel approach for evaluating water quality within a complex riverine zone in South Korea: Han River that encompasses five river streams situated at each junction of North and South streams (including Gyeongan Stream) that ultimately leading towards Paldang Lake. By utilizing the monthly water characteristic data from the year 2013–2022 across 14 different locations, the significant seasonal trends and potential influences on water quality are identified. The water quality here is calculated with the proposed method of sub-index water quality index (s-WQI). A combinatorial prediction approach of s-WQI for each location is conducted through a collective of data preprocessing approaches including Hampel filtering and feature selection in prior to the machine learning predictions. In return, light gradient boosting (LGB) is the most accurate predictor by outperforming other prediction algorithms, especially through LGB-Pearson and LGB-Spearman combinations for North and South stream intersections, and LGB-Pearson for Paldang Lake. To further evaluate the robustness of this evaluation and extending the results to a foreseeable scenario, a seasonal based Monte-Carlo Simulation with 10,000 attempts targeting the water characteristic distributions obtained from each location considered are carried out to identify the risk bounds within. The results are further interpreted with SHAP analysis on identifying the contributions of each water characteristics towards the water quality through local and global spectrum. This research yields practical implications, offering tailored strategies for water quality enhancement and early warning systems. The integration of AI-based prediction and feature selection underscores the transformative potential of computational techniques in advancing data-driven water quality assessments, shaping the future of environmental science research.

A generalized mathematical modeling and performance analysis of GaAs, m-Si and Ge single - junction solar PV cells

Earlier theoretical approaches have analyzed the performance of semiconductor materials for the influence of temperature and band gap which is implemented in solar photovoltaic systems. This paper investigates the detailed mathematical analysis of single junction semiconductor materials such as Gallium Arsenide (GaAs), mono-crystalline Silicon (m-Si) and Germanium (Ge) solar cells parameters namely, Band gap (Eg), reverse saturation current (Io), shunt resistance (Rsh), Short circuit current (Isc), open circuit voltage (Voc), Fill factor (FF), Maximum Power (Pmax) and Efficiency (Ƞ) for temperature ranging from 273K (≈0°C) to 373K (≈100°C). The optimized cell parameters values are obtained under Air Mass 1.5 Global spectrum (Area = 100cm2 and Irradiance = 100 mW/cm2 for the semiconductor materials (GaAs, m-Si and Ge)) at 300K (≈25°C) are compared to identify the higher efficiency and open circuit voltage. Among the semiconductor materials, the single junction GaAs compound semiconductor material is identified with better efficiency and optimized open circuit voltage. A generalized mathematical model of semiconductor materials is developed using single diode equivalent circuit model under SIMULINK platform and it is used to observe the effect of different temperature, irradiance levels, series resistance and shunt resistance values on the performance of solar cells. The discussion is extended for a 2KW PV array for the semiconductor materials (GaAs, m-Si and Ge) under different temperature and irradiance levels. Evaluating the performance of a PV array, rather than individual cells, provides a more comprehensive understanding of how the chosen semiconductor materials function as part of a larger solar energy system.

Rapid estimation of battery state of health using partial electrochemical impedance spectra and interpretable machine learning

The longevity and efficacy of lithium-ion batteries diminish over time, making accurate estimation of their health state essential. Traditional methods, based on long-term charge and discharge data, are limited in speed and data richness. This study introduces a novel approach using partial Electrochemical Impedance Spectroscopy (EIS) and interpretable machine learning for rapid and precise battery health state estimation. Our method selects partial impedance spectra, in contrast to conventional techniques that rely on global impedance spectra, and applies interpretable machine learning. The SHAP (SHapley Additive exPlanations) framework is used to interpret the contribution of each impedance feature, enhancing model transparency and reliability. Comparative evaluations demonstrate that SHAP-enhanced partial impedance spectra provide higher prediction accuracy and are more quickly obtained than global spectra. This innovative application of interpretable machine learning in acquiring partial impedance spectra represents a significant advancement in battery health state estimation. Our findings propose a new paradigm in employing EIS for battery health analysis, promising enhanced precision and speed in practical applications.

Multiple Dimensions of Functional Traits in Subtropical Montane Mosses

The study of functional traits and their relationship to trade-offs has provided valuable insights into how plants adapt to environmental changes. Nonetheless, further research is necessary to fully comprehend the subtropical montane trade-off patterns in moss functional traits and the impact of environmental gradients on the correlation of these traits. To address this gap, we conducted a study of 11 moss species (7 families, 9 genera) in 54 patches from two subtropical mountain ranges, examining 40 functional traits related to photosynthesis, nutrients, water retention, and architecture. Through principal component analysis (PCA) and bi-variate correlation analysis, our findings reveal a strong correlation between light capture and nutrient assimilation strategies, as evidenced by the coordination between the traits of light capture and nutrient per area along a main principal component. Interestingly, we observed a trend towards smaller leaves and leaf cells in species with higher capacity for photosynthesis and metabolism, consistent with global trait spectra in vascular plants. However, we found that the trade-off between light capture and nutrient assimilation strategies was independent of water-holding capacity at shoot levels. Instead, we found that water-holding capacity was closely associated with nutrient utilization, energy metabolism, chlorophyll synthesis, and the primary process of photosynthesis. Our results highlight the multiple dimensions of functional traits in subtropical montane mosses and demonstrate that variation in these traits is driven by water availability, slope, and canopy density. Overall, our study provides valuable insights into the co-variation of moss traits and how environmental changes may impact mosses and ecosystem function.

Typing supernova remnant G352.7−0.1 using XMM–Newton X-ray observations

ABSTRACT G352.7−0.1 is a mixed-morphology (MM) supernova remnant (SNR) with multiple radio arcs and has a disputed supernova origin. We conducted a spatially resolved spectroscopic study of the remnant with XMM–Newton X-ray data to investigate its explosion mechanism and explain its morphology. The global X-ray spectra of the SNR can be adequately reproduced using a metal-rich thermal plasma model with a temperature of ∼2 keV and ionization time-scale of ∼3 × 1010 cm−3 s. Through a comparison with various supernova nucleosynthesis models, we found that observed metal properties from Mg to Fe can be better described using core-collapse supernova models, while thermonuclear models fail to explain the observed high Mg/Si ratio. The best-fit supernova model suggests a ∼13 M⊙ progenitor star, consistent with previous estimates using the wind bubble size. We also discussed the possible mechanisms that may lead to SNR G352.7−0.1 being an MMSNR. By dividing the SNR into several regions, we found that the temperature and abundance do not significantly vary with regions, except for a decreased temperature and abundance in a region interacting with molecular clouds. The brightest X-ray emission of the SNR spatially matches with the inner radio structure, suggesting that the centrally filled X-ray morphology results from a projection effect.

Highly efficient and versatile daytime radiative cooler based on optimized polymer-ceramic composite fabricated via facile process

Radiative cooling is a carbon-free, zero energy technology that can substitute various energy-consuming cooling systems such as air conditioners and auto-chillers for cooling heat-generating machines. Previous reports on radiative cooling have focused mainly on the cooling performance, thereby overlooking the applicability, structural complexity, manufacturing complexity, and certain physical properties of the cooler. In this study, we present an efficient, applicable, and facile-fabricated radiative cooler composed of poly(vinylidene fluoride-co-hexafluoro propene) (P(VDF-HFP)) and alumina (Al2O3). Apart from being fabricated in the form of a freestanding sheet with 97.5% solar reflectance and 94.8% infrared (IR) emissivity, the P(VDF-HFP)/Al2O3-based radiative cooler (PARC) can be applied to various solid substrates via simple coating techniques. This shows that the PARC can be introduced in architecture, clothing, and other fields that demand cooling. The cooling performance of PARC is experimentally proven by a sub-ambient temperature drop of 10.1 °C during daytime and the cooling power of the PARC is calculated to be 125.6 Wm-2 under the AM1.5 global spectrum, which is superior to that of previously reported radiative cooling emitters.