Local Temperature Research Articles

Modeling of cryogenic heated-tube flow boiling experiments of hydrogen and helium with the Generalized Fluid System Simulation Program

Accurate modeling of cryogenic boiling heat transfer is vital for the development of extended-duration space missions. Such missions may require the transfer of cryogenic propellants from in-space storage depots or the cooling of nuclear reactors. Purdue University in collaboration with NASA has assembled a database of cryogenic flow boiling data points from steady-state heated-tube experiments dating back to 1959, which has been used to develop new flow boiling correlations specifically for cryogens. Computational models of several of these experiments have been constructed in the Generalized Fluid System Simulation Program (GFSSP), a network flow code developed at NASA’s Marshall Space Flight Center. The new Purdue-developed universal correlations cover the full boiling curve: onset of nucleate boiling, nucleate boiling, critical heat flux, and film boiling. These correlations have been coded into GFSSP user subroutines. The fluids modeled in this study are liquid hydrogen and liquid helium. Predictions of local wall temperature and pressure drop are presented and compared to the test data.

TPTNet: A Data‐Driven Temperature Prediction Model Based on Turbulent Potential Temperature

AbstractA data‐driven model for predicting the surface temperature using neural networks was proposed to alleviate the computational burden of numerical weather prediction (NWP). Our model, named TPTNet uses only 2 m temperature measured at the weather stations of the South Korean Peninsula as input to predict the local temperature at finite forecast hours. The turbulent fluctuation component of the temperature was extracted from the station measurements by separating the climatology component accounting for the yearly and daily variations. The effect of station altitude was then compensated by introducing a potential temperature. The resulting turbulent potential temperature (TPT) data at irregularly distributed stations were used as input for predicting the TPT at forecast hours through three trained networks based on convolutional neural network, Swin Transformer, and a graph neural network. By comparing the prediction performance of our network with that of persistence and NWP, we found that our model can make predictions comparable to NWP for up to 12 hr.

Boiling heat transfer of upwind planar water jet impingement on moving hot steel sheet

This study aimed to experimentally investigate the boiling heat transfer characteristics of an upward planar water jet on a horizontally moving stainless-steel sheet with 0.3-mm thickness using an infrared camera-based method. The experiments varied the moving velocity of the solid (1.5, 3.0, and 4.4 m/s), the initial temperature (200–600 °C), and the jet impact velocity (1.08 and 1.32 m/s). The surface heat flux distribution and temperature profile of the solid were found to be asymmetric to the jet impingement line. The temperature drop due to jet impingement ΔTm can be expressed as ∫qs(x)dx/(ρcHVs), where ρ,c,H are the density, specific heat, and thickness of the test sheet, respectively. This quantity depends both the heat flux profile qs(x) and the moving velocity Vs. The solid exhibited large temperature decreases at small moving velocities. The curve of the maximum heat flux, defined as the peak value in each experiment, exhibited similarities to a pool boiling curve and was significantly dependent on the local temperature of the solid. The empirical correlations for predicting the maximum heat flux in the nucleate and film boiling regions were constructed as qmax=50100VS-0.169vj0.377ΔTsat1.48 W/m2 and qmax=34500VS0.014vj0.5ΔTsat W/m2, respectively, where vj, and ΔTsat are the jet impact velocity and wall superheat.

Thermal Characteristics of Oil Film in Critical Lubrication State of Heavy Hydrostatic Bearing

Background: Lubrication failure has always been a concern in the research of heavy hydrostatic bearings. A preliminary study found that under a certain working condition, the heavy hydrostatic bearing will appear in the condition of zero local flow of oil film, which is called a critical lubrication state. It produces a significant thermal accumulation effect and affects the lubrication performance of the bearing. Objective: This paper takes Q1-205 double rectangular cavity hydrostatic thrust bearing as the research object and adopts the research method combining theoretical analysis, simulation, and experimental test to analyze the temperature rise and characteristics of the bearing when it is in critical lubrication state under different working conditions. Methods: The finite volume method is used to simulate the critical lubrication state under the condition of common load and rotating speed, and the corresponding operating parameters of the critical lubrication of heavy static support are obtained. Results: It is found that when the oil film is in the critical lubrication state, the low temperature zone is concentrated at the bearing position of the oil film, while the high temperature zone is concentrated at the counter-current side sealing edge Conclusion: The oil film's local temperature rise is intensified, and the heat accumulation phenomenon is serious because the heat cannot be taken away in time.

Multivariate nonlinear analysis of climatological variables and its effect on local temperature.

Atmospheric temperature describes the specific heat content of the air at particular places and times. In this sense, the presented work proposes a multivariable system that takes data sets of different climatological variables, with the aim of regulating the temperature level. The diversity in climatological variables significantly affects precipitation, humidity, wind speed and temperature. Thus, a study has been carried out on these variables in terms of nonlinear dynamics. The aim of the work is to obtain a better understanding of the dynamics of local climatological variables. On the other hand, due to the fact that the time series analyzed are small, the analysis becomes complex, at the moment of joining all the variables in conjunction, and processing them by means of multivariate statistical prediction methodologies. The results have shown the mean values in the different variables with which a temperature is maintained between and , which shows possible work with multi-objective optimization of the obtained model.

Microbial nitrogen nutrition links to dissolved organic matter properties in East African lakes

East African lakes, especially soda lakes, are home habitats for massive numbers of wildlife such as flamingos, mammals, and fishes. These lakes are known for their high primary production due to local high temperatures, light intensities, and alkalinity (inorganic carbon). However, these lakes, normally within remote areas, receive low nutrient inputs. Ammonium (NH4+) recycling and/or nitrogen fixation can become the major N supply mechanisms for phytoplankton. However, the driving forces on microbial N nutrition in lakes with minimal anthropogenic disturbance remain poorly understood. Using stable isotope tracer techniques, NH4+ recycling rates were measured in 18 lakes and reservoirs in East Africa (Tanzania and Kenya) during the dry season in early 2020. Three functional genes (nifH, gdh, and ureC) relating to microbial N nutrition were also measured. The regeneration of NH4+ supported up to 71 % of the NH4+ uptake. Positive community biological NH4+ demands (CBAD) for all lakes and reservoirs indicate an obvious N demand from microbial community. Our study provides clear evidence that microbial NH4+ uptake rates linked closely to the dissolved organic matter (DOM) properties (e.g., the absorption coefficient at 254 nm, percents of total fluorescence intensity contributed by microbial humic-like and protein-like components) and that water residence time drives microbial NH4+ recycling by regulating the duration of in-lake DOM processing and influencing algal growth. Phytoplankton, especially those of Cyanophyceae, showed maximum biomass and higher NH4+ recycling rates at a certain range of water residence time (e.g., 5–8 years). However, CBAD showed a decreasing trend with longer water residence time, which may be influenced by changes in the algal community composition (e.g., % Cyanophyceae vs. % Bacillariophyceae). These results indicate that DOM dynamics and the water residence time have the potential to facilitate the understanding of microbial nitrogen supply status in East African lakes.

On the Role of the Building Envelope on the Urban Heat Island Mitigation and Building Energy Performance in Mediterranean Cities: A Case Study in Southern Italy

The urban heat island (UHI) effect is one of the largest climate-related issues concerning our cities due to the localized temperature increase in highly urbanized areas. This paper aims to investigate the impact of UHI mitigation techniques in promoting climate resilience, by reducing urban air temperatures and cooling energy consumption in buildings. To this end, four mitigation solutions regarding the building envelope—green roofs, green walls, cool roofs, and cool walls—were investigated for the city of Bari in Southern Italy and compared with the current baseline scenario. Hence, five scenarios were simulated—using the ENVI-met microclimate software—during three representative summer days, and the resulting microclimate changes were assessed. Based on these analyses, new climate files—one for each scenario—were generated and used as input to run energy simulations in EnergyPlus to estimate the building cooling consumption. Coupling the microclimate and the consumption outcomes, the mitigation strategies were evaluated from both an urban and building point of view. The study shows that urban characteristics, mainly geometry and materials, are crucial for the UHI phenomenon. All the applied technologies seem to be effective. However, green walls proved to be more efficient in reducing outdoor temperatures (1 °C reduction in daily temperatures), while cool walls performed better in reducing cooling energy consumption, with an overall saving of 6% compared to the current scenario.

Enhancing Sustainable Thermal Comfort of Tropical Urban Buildings with Indoor Plants

Rapid urbanization exacerbates the urban heat island effect, raising local temperatures and endangering residents’ health and well-being. The decreasing green spaces resulting from urbanization necessitate global action focused on reducing heat island intensity and addressing heat stress. Urban green infrastructure (UGI) offers solutions for enhanced comfort and reduced pollution through passive methods. Various large-scale UGI projects have been implemented to regulate temperature and improve air quality in urban areas. More research on smaller green spaces is essential to improve the microclimate in space-constrained urban cities. This experimental study examines the thermal effectiveness of potted plants located on balconies of a mid-rise residential building in Chennai, India. The study aims to enlighten balcony greening’s role in reducing heat stress by monitoring temperature and humidity indoors and outdoors, with and without potted plants at similar solar radiation. Potted plants significantly lowered indoor air and surface temperatures by about 3 °C. Thus, balconies offer untapped potential for green interventions that are often unnoticed in tropical climates like India. The challenges in the installation and maintenance of UGI hinder the widespread adoption of UGI even though UGI positively influences residential well-being. The significant findings benefit urban planners and architects, enlightening strategies to enhance urban thermal comfort and mitigate heat stress through small-scale and cost-effective green interventions. This research contributes to sustainable urban development in tropical climates, aligning with several UN Sustainable Development Goals (SDGs), including SDGs 3, 7, 11, 13, and 15.

A fast and high-fidelity multi-parameter thermal-field prediction system based on CFD and POD coupling: Application to the RPV insulation structure

The thermal performance of the reactor pressure vessel (RPV) insulation structure plays a crucial role in ensuring the operational safety of the reactor. In this study, a fast and high-fidelity prediction system is proposed, which is based on the snapshot ensemble generated by the computational fluid dynamics (CFD) method and incorporates proper orthogonal decomposition (POD) as its core component to predict the heat transfer performance of the RPV insulation structure. The proposed thermal-field prediction system is employed to examine the impacts of air inlet temperature, mass flow rate, and RPV outer wall temperature on the thermal performance parameters: average heat flux qout and temperature Tout of the RPV insulation outer wall, as well as the maximum local temperature Tmax of the concrete pit inner wall. The POD predictive results showcase that the maximum relative deviations of qout, Tout and Tmax for eight sets of off-design cases are 2.25 %, 3.04 %, and 2.37 %, respectively. Meanwhile, the maximum values of qout, Tout and Tmax are 90.13 W·m−2, 44.69 ℃, and 87.51 ℃, respectively. They are all less than their prescribed limits. The field distributions of heat flux and temperature in off-design cases exhibit consistency between the CFD and POD methods. Notably, in comparison to the CFD simulation method, the POD method capturing more than 99.9 % energy demonstrates significant computational time savings for 99.87 %. Moreover, a multivariate linear regression (MLR) model for thermal performance parameters is established based on the POD prediction datasets. The results indicate that the maximum relative deviations between the POD and MLR methods in predicting qout, Tout and Tmax for the 72 sets of test cases are 5.1 %, 7.9 % and 7.1 %, respectively. Meanwhile, 99.99 % CPU time is reduced for MLR model compared with CFD method. Obviously, the predictive system showcases excellent accuracy and efficiency in forecasting.

Reduced size in a montane butterfly at its warm range boundaries

Abstract Variation in insect size is often related to temperature during development and may affect the persistence of populations under future climate warming if smaller individuals have reduced fitness. Montane species are particularly vulnerable to climate‐driven local extinctions due to range retractions at their warm range margins, and so we examined spatial and temporal variation in body size in the butterfly Erebia epiphron (Lepidoptera: Nymphalidae) in the United Kingdom, where it is restricted to two montane regions in England and Scotland. We examined spatial and temporal variation in body size in relation to temperature. We sampled 19 populations (6–15 individuals per population) in England and Scotland between 2018 and 2019 spanning elevations from 380 to 720 m and examined museum specimens collected between 1890 and 1980. We examined individual body size (forewing length) and its relationship with the local temperature of sites, as well as temporal variation in body size over the last century in relation to the temperature during larval development. The forewing lengths of field‐collected individuals in England were on average 7%–8% smaller than in Scotland (England, mean = 14.9 mm, Scotland, mean = 15.9 mm), and warmer sites also had smaller individuals (0.13 mm reduction in wing length per 1°C increase in local site mean temperature). However, we found no effect of temporal temperature variation on body size changes during larval development. The observed smaller body size in English populations could have impacts on fecundity and dispersal ability. Future work should seek to understand the life‐cycle lengths, genetics and phenotypic plasticity of these two populations to evaluate potential explanations for regional differences.

Laser-induced spectral tuning of single quantum dots embedded into microposts cladded with HfO2

Our work investigates the precise tuning of InGaAs quantum dots (QDs) embedded into microposts by leveraging HfO2 crystallization-induced micro-strain via laser annealing. We investigate the efficacy of laser annealing power as a parameter for spectral control, achieving a notable blue shift of QD emissions of up to 5 meV. Through comprehensive Raman thermometry, we reveal consistent dependencies in laser-induced heating relative to micropost diameter, with larger microposts exhibiting superior heat dissipation capabilities and smaller tuning range. For instance, a 5.0 μm micropost demonstrates a maximum local temperature increase of 260 K at 1.82 mW of annealing power, compared to 435 K for a 1.1 μm diameter micropost under the same conditions. By correlating local temperatures derived from the longitudinal optical phonon linewidth of the Raman spectra, with QD emission line blue shift at specific laser powers, the tunability across differing post diameters is studied. Our findings underscore the potential of strain-tuning QDs through laser-induced HfO2 crystallization, offering avenues for scalable resonant single-photon sources applicable in superradiance and multi-photon interference scenarios.

Hyaluronic acid-targeted dual-bubble/photothermal-driven nanomissile for enhanced “four-in-one” anti-tumor strategy

The dense extracellular matrix and high interstitial pressure affect the diffusion of nanodrug in tumor tissue, resulting in a small range of action of the active components in nanodrug, thereby affecting its anticancer efficacy. In order to enhance the diffusion ability of nanodrug, a dual-bubble/photothermal-driven nanomissile (HA@MnO2@TA/Fe/ICG/TPZ, HMTAFIT) was designed through “four in one” anti-tumor strategy. Harnessing the capabilities of hyaluronic acid, a biomacromolecule, the nanomotor transforms into a nanomissile, targeting cancer cells with precision. The oxygen generated by the reaction of manganese dioxide with hydrogen peroxide and the local temperature rise of indocyanine green under near-infrared light endow HMTAFIT with the ability of bubble/photothermal dual-driven, and the outermost layer of modified hyaluronic acid incubates the targeting properties of HMTAFIT which could avoid damage to normal cells. The bubble/photothermal-dual-driven increases motion speed of HMTAFIT by 13.8 μm/s, and the enhanced “four in one” anti-tumor strategy effectively improved the anticancer efficacy. The precision-guided nanomissile boasts the capability to eliminate deep-seated cancer cells and overcome multidrug resistance via optimized diffusion and a cutting-edge “four-in-one” approach.

Numerical Study on the Effects of Variable Mushy Zone Constants on Fluid Flow and Heat Transfer in Bloom Mold

In previous studies, the mushy zone constant was assigned a fixed value or a value associated with dendrite characteristics at room temperature. However, elucidating the constitutive behaviors of mushy dendrites at high temperatures using solid dendrite characteristics at room temperature poses challenges. In this study, a numerical model was constructed to investigate the effects of variable mushy zone constants on fluid flow and heat transfer in the mold. The results revealed that “Zone Z‐1/2” exhibited a thinner solidified shell compared with the other zones, indicating a close correlation between heat transfer and thickness differences. Under default simulation conditions, the liquid steel cooled rapidly, leading to the dissipation of superheat. The mushy zone exhibited a wide range, with almost no pure liquid phase present in the mold. In cases 2, 3, and 4 within “Zone Z‐1/2”, the average thickness of the solidified shell gradually increased during solidification, indicating the absence of evident remelting phenomena in the mold. As the variable mushy zone constants increased, the local temperature gradient increased, while the local cooling rate decreased. The accuracy of the numerical model was validated through actual measurements and empirical formulas. The simulation results for case 4 exhibited a high degree of fitting.

Numerical Study of the Influence of Regenerator Structure on the Performance of Hot Blast Stoves

The properties and arrangement of checker bricks in regenerators are crucial for the heat exchange process of hot blast stoves. In this study, a 3D fluid flow heat transfer model is established to analyze the influence of three regenerator layered structures on the combustion and air supply performance of hot blast stoves. The results show that a “silica bricks–high-alumina bricks–clay bricks” three-layer arrangement in regenerators produces a “thermal conduction hindrance effect” at the interface between silica and high-alumina bricks during the 2 h combustion period, which raises the local temperature and improves air supply performance. Compared to the “silica bricks–clay bricks” and “high-alumina bricks–silica bricks–clay bricks” structures, this setup increases the maximum air supply temperature difference to 23 and 64 K, respectively, and extends the effective air supply time by 80 and 320 s, respectively. However, the “thermal conduction hindrance effect” diminishes over longer combustion periods, and by 4 h, the performance across all structures becomes increasingly consistent. Additionally, the study suggests that the temperature level and distribution in the upper part of the regenerator are the key factors determining the air supply performance of hot blast stoves.

Examining the non-linear relationship between urban form and air temperature at street level: A case of Hong Kong

The relationship between urban form and localized air temperature has been studied extensively using linear regression. However, the findings remain inconsistent, and few studies have explored alternative data modeling techniques. With the rise of machine learning, there is an opportunity to explore new methods in urban climatology research. This study aims to test the hypothesis that machine learning models, rather than linear regression, can better explain and predict urban air temperature fluctuations in space and time. Measurements of air temperature were conducted at street level in Hong Kong. Urban form characteristics surrounding the measurement locations were extracted from geo-spatial databases and street view imagery. The datasets were then used to test the performance of linear regression, Artificial Neural Network (ANN), and Random Forest (RF) in predicting the spatial-temporal temperature fluctuations. The results indicate that the relationships between urban form and air temperature are predominantly non-linear. Both ANN and RF outperformed linear regression in prediction, with an MAE of 0.43 °C and 0.33 °C respectively. This study highlights the potentials of machine learning models in advancing knowledge of the impact of urban form on localized air temperature.

Oxygen-defect bismuth oxychloride nanosheets for ultrasonic cavitation effect enhanced sonodynamic and second near-infrared photo-induced therapy of breast cancer

Sonodynamic therapy (SDT) relies heavily on the presence of oxygen to induce cell death. Its effectiveness is thus diminished in the hypoxic regions of tumor tissue. To address this issue, the exploration of ultrasound-based synergistic treatment modalities has become a significant research focus. Here, we report an ultrasonic cavitation effect enhanced sonodynamic and 1208 nm photo-induced cancer treatment strategy based on thermoelectric/piezoelectric oxygen-defect bismuth oxychloride nanosheets (BNs) to realize the high-performance eradication of tumors. Upon ultrasonic irradiation, the local high temperature and high pressure generated by the ultrasonic cavitation effect combined with the thermoelectric and piezoelectric effects of BNs create a built-in electric field. This facilitates the separation of carriers, increasing their mobility and extending their lifetimes, thereby greatly improving the effectiveness of SDT and NIR-Ⅱ phototherapy on hypoxia. The Tween-20 modified BNs (TBNs) demonstrate ∼88.6 % elimination rate against deep-seated tumor cells under hypoxic conditions. In vivo experiments confirm the excellent antitumor efficacy of TBNs, achieving complete tumor elimination within 10 days with no recurrences. Furthermore, due to the high X-ray attenuation of Bi and excellent NIR-Ⅱ absorption, TBNs enable precise cancer diagnosis through photoacoustic (PA) imaging and computed tomography (CT).

Utilizing GaoFen-2 derived urban green space information to predict local surface temperature

Urban green spaces (UGS) significantly influence the distribution of surface heat and play a crucial role in regulating surface temperature. However, the quantitative relationship between UGS and surface temperature remains unclear, necessitating further research. This study aims to predict surface temperature based on green space information from GaoFen-2 satellite data. To achieve this, GaoFen-2 data were utilized to obtain spatial distribution and vegetation growth status in Urumqi, Xinjiang. Three machine learning models such as Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Regression Tree (GBRT) were constructed to predict surface temperature. Results indicated that UGS information extracted from GaoFen-2 data using the U-Net semantic segmentation model successfully predicted surface temperature. Among the three machine learning models, GBRT exhibited the highest predictive accuracy with an Radj2 of 0.81, RMSE of 0.44, and RPD of 2.29, followed by RF (Radj2 of 0.80, RMSE of 0.45, and RPD of 2.22), and SVM (Radj2of 0.79, RMSE of 0.47, and RPD of 2.15), In addition, a variable importance assessment reduced the original 44 variables to 28, maintaining predictive accuracy with the GBRT model achieving an Radj2 of 0.81, RMSE of 0.43, and RPD of 2.3. Our study demonstrates the effectiveness of using vegetation information derived from GaoFen-2 to predict surface temperature. This approach provides valuable recommendations for the layout of UGS in urban areas and serves as a comprehensive reference for urban planning and real estate development.

Tracing Holocene temperatures and human impact in a Greenlandic Lake: Novel insights from hyperspectral imaging and lipid biomarkers

Global warming particularly impacts terrestrial and aquatic ecosystems in the Arctic. To constrain the sensitivity of Arctic lakes and make meaningful predictions about future change under global warming, we need to examine their response to previous warm phases. Lake sediments from Greenland's deglaciated area offer valuable archives to investigate past climate variability and associated lake changes.Here, we applied hyperspectral imaging and lipid biomarker thermometry to a Holocene-length sediment record from Lake 578 in the Eastern Settlement of the Norse (61.08° N, 45.62° W; ∼155 m a.s.l) to investigate local temperature, productivity, and anoxia histories. We calibrated branched glycerol dialkyl glycerol tetraethers (brGDGTs) with summer mean water temperatures (SMWT) using a previously published site-specific calibration and analyzed pigment fluxes based on hyperspectral imaging. Notably, the anoxia reconstructions were corroborated with two independent proxies (GDGT-0/Crenarchaeol and bacterio pheophytins). We investigated the lake's environmental history and identified periods of significant change by employing generalized additive models (GAMs).Our results reveal significant transitions in Lake 578 driven both by natural climate shifts and anthropogenic impacts. During the early Holocene, low SMWT and productivity coupled with high anoxia suggest strong seasonality and prolonged inverted thermal stratification, possibly enhanced by extended ice cover. The mid-Holocene showed higher SMWT and productivity along with low anoxia, indicating a dimictic lake system. The early Holocene temperature rise lagged that of to the Northern Hemisphere, but closely followed the Atlantic-Fennoscandian stack. The Holocene Thermal Maximum (7.5–4.5 cal ka BP) aligns with other regional reconstructions. After 3 cal ka BP, we observed a Neoglacial cooling characterized by increased anoxia and reduced temperatures due to enhanced stratification. At around 1.0 cal ka BP, Lake 578 saw a surge in productivity and anoxia, which we attribute to land use and lake damming by the Norse. Despite a post-Norse decline in productivity and disappearance of anoxia, the lake never reverted to its pre-Norse state, with modern sheep farming further intensifying productivity in recent decades. While early Holocene anoxia resulted from natural cold temperature stratification, anoxia during the Norse period was anthropogenically induced.This research underscores the value of integrating lipid biomarkers with hyperspectral imaging for detailed reconstructions of changes within Arctic lakes. It provides crucial insights for anticipating the ecologic and climatic resilience of Arctic lakes to ongoing global warming and anthropogenic influence.

Measurement of local boiling heat transfer coefficient (HTC) within a brazed plate heat exchanger (BPHE) and heat transfer assessment of low-GWP R134a replacements

This article shows the local boiling HTCs of R134a and three low-GWP replacements, R1234yf, R1234ze(E) and R152a within a (BPHE). The experimentation was performed in a test-section specially set up for the measurement of the local two-phase HTC at a saturation temperature of 10 °C, with a refrigerant mass velocity from 9.5 to 35.2 kg m−2 s−1, incoming vapour quality between 0.2 and 0.3 and almost complete vaporisation. Both nucleate boiling and forced-convection boiling play an important role in these tests, depending, mainly, on the location along the plate. The comparison with theoretical models shows that both Longo et al. (2015) and Amalfi et al. (2016) models reproduce very well the experimental HTCs. The measurement of local temperature profiles within the BPHE allows to couple the evaluation of thermal and hydraulic performances of refrigerants into a single parameter named Total Temperature Penalty (TTP). The TTP analysis highlights similar boiling performance for R134a, R1234yf and R152a refrigerants, while R1234ze(E) displays worse performance.

Pd octahedra nanocubes mediated photo-fenton catalytic performance for sustainable degradation of methylene blue

This research utilized a typical solvothermal approach, by employing Na2PdCl4 as the synthetic precursor, L-ascorbic acid (AA) as the reductant agent, and polyvinylpyrrolidone (PVP) as the practical surfactant, palladium (Pd) octahedra was finally effectively synthesized by the hydrothermal and solvothermal methods. Subsequently, characterizations of Pd octahedra were conducted by using TEM, XPS, XRD, and other analytical methods. Whereafter, Pd octahedra displayed a consistent shape and size, featuring a distinctive tip-shaped structure, which conferred upon them outstanding optical absorption properties within the near-infrared (NIR) region. Consequently, under NIR laser exposure, Pd octahedra could elevate local reaction temperature via in-situ photothermal conversion, thereby stimulating the formation of active sites of Fenton catalysts. The Photo-Fenton catalytic activity of Pd octahedra was investigated by using methylene blue (MB) as a model pollutant. The degradation efficiency of MB reached 94.8 % within 35 min under 808 nm laser irradiation, and the removal rate of MB by Pd octahedra could still reach more than 90 % after five cycles of use. All the results manifest that Pd octahedra exhibit excellent purification performance for MB dyestuff wastewater.