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On the other hand, the accessible multiple oxidation states of reactive Cu elements (Cu SP 0 sp , Cu SP I sp , Cu SP II sp , and Cu SP III sp ) within Cu-based catalysts can enable targeted catalytic reactions that proceed via both single- and multi-electron transfer. Dang et al. [[5]] synthesized self-supported Cu/Cu SB 3 sb P nanoarrays through a low-temperature phosphidation process, which were directly grown on the surfaces of Cu nanosheets from Cu/CuO nanoarrays precursor. Compared with the Cu(111) surface, the Cu-skin Cu SB 3 sb Au(111) surface showed higher stability of O SB 2 sb adsorption and a lower dissociation energy barrier of the adsorbed O SB 2 sb . [Extracted from the article]

The intrinsic activity of single-atom catalysts is influenced by the local electronic structures of metal centers, with existing modulation strategies limited to adjacent atomic coordination. However, the impacts of support surface geometry on local bonding environment, and thus electronic structures of single-atom centers have rarely been known. Here, we prepared highly curved B,N co-doped carbon-supported ruthenium catalyst with an ultra-low Ru loading of 0.4 wt%, which exhibited an ultrahigh turnover frequency (TOF) of 10 H 2 s−1 (38 times that of Pt/C) and superior stability in alkaline hydrogen evolution reaction (HER). We found that curving support surface induced the strain, resulting in 1.5% compressed Ru-N and 4% stretched Ru-B bonds, leading to the accumulation of positive charge and quenching of spin polarization at Ru sites, thereby achieving the optimal binding of H* and enhanced performance for HER. This work highlights the significant support effects upon the structural design of active sites. [Display omitted] • Two types of B, N co-doped carbon-supported Ru site catalysts with and without support surface curvature were synthesized. • Curving the support surface induces strain and thereby changes the local bond length at the Ru sites. • Regulating bond length increases the aggregation of positive charges and suppresses spin polarization at the Ru sites. • The B, N co-doped carbon-supported Ru site catalyst with curvature achieves optimal H* binding strength for HER. • The turnover frequency of Ru sites with curvature is significantly higher than those without surface curvature. [ABSTRACT FROM AUTHOR]

Accurate evapotranspiration (ET) estimation is paramount for effective agricultural water management. As the key parameter of surface resistance (r s)-based ET models, the direct acquisition of canopy resistance (r c) remains challenge. Moreover, the applicability of r c models and r s -based ET models has not been fully validated on kiwifruit. To address above problems, this study investigates tree growth patterns, water consumption change, and soil water content (SWC) dynamics under different irrigation strategies based on a two-year experiment in Northwest China. Subsequently, the Whale Algorithm (WOA), Quantum Whale Algorithm (QWOA), and Differential Whale Algorithm (DWOA) were employed to optimize Jarvis-type r c models (JA) constructed by different constraint functions of SWC. After internal comparison within Jarvis-type models and external comparison with Kelliher-Perrier (KP), Faria (FA), and Stannard (ST) model, the recommended r c model was integrated into Penman-Monteith (PM), Shuttleworth-Wallace (SW), and Clumping (CL) to select most suitable kiwifruit ET model. The results indicated that the water stress coefficient is suitable for JA model as a constraint function of SWC, achieving R2, NSE, RMSE, and MAPE of 0.782–0.805, 0.672–0.737, 654.191–3396.594 s m−1, and 0.102–0.183 under sufficient irrigation strategy, respectively. Under deficit irrigation strategies, the corresponding R2, NSE, RMSE, and MAPE ranged 0.886–0.917, 0.828–0.885, 325.736–3251.434 s m−1, and 0.111–0.166, respectively. Besides, DWOA is the best algorithm during whole growth period, which improved original JA by 13.2–32.9% for R2, 23.0–60.2% for NSE, 26.8–36.8% for RMSE, and 17.5–36.2%, respectively. Regarding r s -based ET models, the CL performed best under various irrigation conditions, with values of R2, NSE, RMSE, and MAPE ranging 0.908–0.961, 0.842–0.947, 0.311–0.808 mm, and 0.088–0.197, respectively. Overall, the CL integrated with DWOA-JA3 is most recommended for estimating kiwifruit ET under different irrigation strategies, which is helpful for orchardists to enhance agricultural water utilization efficiency and promote sustainable kiwifruit production. • Soil water deficit coefficient was accepted as constraint function to develop canopy resistance model. • Jarvis-type models were optimized using Quantum and Differential Whale Optimization Algorithm. • The accuracy of canopy resistance model was evaluated under different irrigation strategies. • Surface resistance-based evapotranspiration models were compared for kiwifruit in Northwest China. [ABSTRACT FROM AUTHOR]

Accurate reference crop evapotranspiration (ET 0) estimation is essential for agricultural water management, crop productivity, and irrigation systems. As the standard ET 0 estimation method, the Penman-Monteith equation has been widely recommended worldwide. However, its application is still restricted to comprehensive meteorological data deficiency, making the exploration of alternative simpler models for acceptable ET 0 estimation highly meaningful. Concerning the aforementioned requirement, this study developed the novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, and regression component, to estimate ET 0 based on radiation-based (R n -based), humidity-based (RH-based), and temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal and external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), and empirical models, the result indicated that MA-CNN-BiLSTM achieved superior precision, with values of Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 mm d−1 for internal strategy and 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 mm d−1, 0.294–0.699 mm d−1 for external strategy. Specifically, R n -based MA-CNN-BiLSTM excelled in the temperate continental zone (TCZ) and mountain plateau zone (MPZ), while RH-based MA-CNN-BiLSTM yielded best precision in others. Furthermore, the internal strategy was superior to external strategy by 2.74–106.04% for R2, 1.11–120.49% for NSE, 1.41–40.27% for RRMSE, 1.68–45.53% for RMSE, and 1.21–38.87% for MAE, respectively. In summary, the main contribution of the present study is the proposal of a novel LSTM-type ET 0 model (MA-CNN-BiLSTM) to cope with various data-missing scenarios throughout China, which can provide effective support for decision-making in regional agriculture water management. • MA-CNN-BiLSTM model provides the highest accuracy for reference crop evapotranspiration estimation across China. • Machine learning and empirical models performed better with internal strategy than external strategy. • Radiation-based models excelled in temperate continental and mountain plateau zone, while humidity-based excelled in others. • Multi-head attention improved Long Short-Term Memory network type model as intricate relationship processor. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives of the Penman - Monteith equation, empirical methods have been widely recommended worldwide. However, its application is still limited to parameters localization varied with geographical and climatic conditions, therefore developing an excellent optimization algorithm for calibrating parameters is very necessary. Regarding the above requirement, the present study developed a novel improved Grey Wolf Algorithm (MDSL-GWA) to optimize the most recommended ones among three types of ETo methods. After the optimization performance comparison among Least Square Method (LSM), Genetic Algorithm (GA), Grey Wolf Algorithm (GWA), and MDSL-GWA in four climatic regions of China, this study found that the Priestley-Taylor (PT) method was the best radiation-based (R n -based) method and achieved better performance in temperate continental region (TCR), mountain plateau region (MPR), and temperate monsoon region (TMR) than other types. While the temperature-based (T-based) Hargreaves-Samani (HS) method performed best in subtropical monsoon region (SMR), further attaching better performance among the same type in TMR and TCR, while the Oudin method was the best T-based method in MPR. Moreover, the Romanenko method was better humidity-based (RH-based) in TCR and MPR, whereas the Brockamp-Wenner method exhibited higher in SMR and TMR. Furthermore, despite intelligence optimization algorithms significantly enhancing original ETo methods, the MDSL-GWA achieved best performance and outperformed other algorithms by 4.5–29.6% in determination coefficient (R2), 4.7–27.3% in nash-sutcliffe efficient (NSE), 3.7–44.4% in relative root mean square error (RRMSE), and 3.1–56.2% in mean absolute error (MAE), respectively. After optimization, the MDSL-GWA-PT was the most recommended ETo method in TMR, TCR, and MPR, and the median values of R2, NSE, RRMSE, and MAE ranged 0.907–0.958, 0.887–0.925, 0.083–0.103, and 0.115–0.162 mm, respectively. In SMR, the MDSL-GWA-HS produced the best ETo estimates, with median values of R2, NSE, RRMSE, and MAE being 0.876, 0.843, 0.112, and 0.146 mm, respectively. In summary, this study recommended the best ETo method and algorithms using accessible data in four climatic regions of China, which is helpful for decision-making in effective management and utilization of regional agricultural water resources. • Hargreaves-Samani excelled in subtropical monsoon, but Priestley-Taylor was best elsewhere. • Brockamp-Wenner performed better as RH-based in eastern China, but vice versa for Romanenko. • Intelligence optimization algorithms improved the original version of empirical ET O methods. • Improved Grey Wolf Algorithm provided most suitable parameters for empirical ET O methods. [ABSTRACT FROM AUTHOR]

Accurate estimates of plant transpiration (T sf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (r c), the Penman-Monteith (PM) model is commonly used in T sf modeling. However, the r c is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the r c of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (R n), and air temperature (T a), and of soil water content (θ a) on the accuracy of JA sub-models to calculate apple tree r c and T sf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate r c. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of T sf estimates with five empirical r c models, combined with the PM model. The results showed that sub-model JA 3322 , i.e., the third constraint for VPD, the third constraint for R n , the second constraint for T a , and the second constraint for θ a attained the best estimate of r c with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m-1 and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m-1 and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of r c estimation, where the median R2 and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of r c , followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree T sf , and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously. • Jarvis was superior to other empirical models for canopy resistance estimation. • Jarvis segmented by canopy growth stage gave a more accurate estimation. • Finding the most appropriate Jarvis model for apple tree canopy resistance. • Intelligence optimization algorithms improved the accuracy of the Jarvis models. [ABSTRACT FROM AUTHOR]

• Hierarchical Co-embedded porous carbon microspheres were controllably synthesized. • The Co-based catalyst showed superior performance beyond noble-metal-based catalysts. • The partially uncovered and totally in-built Co species were likely the active sites. The direct pyrolysis of metal organic frameworks (MOFs) has recently provided an effective method to prepare various metal-incorporated composite carbon materials. In this work, hierarchical Co-embedded N-doped porous carbon microspheres were synthesized by controlled pyrolysis of bimetallic Zn-Co Prussian blue analogues (PBAs), in which the metallic Zn component served as thermally removable template and ligand [Co(CN) 6 ]3− served as both Co and N sources. When devoted to catalyzing the reduction of 4-nitrophenol (4-NPh) into 4-aminophenol (4-APh), the optimized catalyst exhibited a large apparent rate constant of 1.35 min−1 and high turnover frequency (TOF) of 0.0204 s−1, which was superior to the majority of noble-metal-based catalysts. Besides, a home-made continuous flow catalysis system was readily constructed. Control experiments confirmed that both partially uncovered and totally in-built active metallic Co species acted as catalytically active sites. This work might provide a further understanding of the identification of active sites within metal-incorporated carbon-based catalysts. [ABSTRACT FROM AUTHOR]

A unique network of a-NiCo-MOFNWs-P is developed by a two-step pyrolysis strategy, which exhibits superior activity and long-term durability in methanol electrooxidation reactions. [Display omitted] Developing high-performance and low-cost electrocatalysts toward methanol oxidation reaction (MOR) is essential for fuel cell applications. Herein, we report a defect engineering strategy integrating amorphization and phosphorization to construct directly interconnected networks of amorphous NiCo-based metal–organic framework nanowires (a-NiCo-MOFNWs) with phosphorus (P) doping. The resulting P-doped a-NiCo-MOFNWs (a-NiCo-MOFNWs-P) network displays superior MOR efficiency and long-term durability over 1000 cyclic voltammetry (CV) measurements. The special structure of directly interconnected networks and the synergistic effect between the amorphous MOFs and dispersed phosphorus species give rise to abundant exposed active sites, accelerated electron transport, and increased porosity for mass transfer, thus boosting the reaction kinetics of MOR. This work provides additional insights into the network assembly and structural evolution of one-dimensional (1D) MOFs, and also opens up new avenues for the design of highly reactive and robust non-precious metal-based electrocatalysts. [ABSTRACT FROM AUTHOR]

Deficit drip irrigation is an advanced agricultural technology widely promoted and applied around the world to maintain/increase yield and quality while simultaneously minimizing water consumption. However, there is still a lack of comprehensive research on the effects of deficit drip irrigation at different stages on apple yield, water productivity, and quality. The aim was to investigate the effects of deficit drip irrigation treatments (DDITs) on apple yield, quality, and water productivity (WP), and optimize the deficit drip irrigation strategy for apples in the Loess Plateau of China. a two-year field experiment was conducted with 17 irrigation treatments, including a control treatment (CK, full irrigation (FI)) and four DDITs (D15%, D30%, D45%, D60%) during the bud burst to leafing stage (I), flowering to fruit set stage (II), fruit expansion stage (III), and fruit maturation stage (IV). The optimal irrigation management was determined using the fuzzy Borda combined model, which was based on four different single evaluation models of the TOPSIS method, principal component analysis (PCA), grey correlation analysis (GCA), and membership function analysis (MFA). The DDITs at stage IV decreased midday leaf water potential (Ψs) by 2.57–20.00% compared with CK, and the difference of Ψs between IV-D30%, IV-D45%, IV-D60%, and CK were significant in two years (P<0.05). DDITs at stages I, II, and IV improved fruit weight (FW), fruit shape index (FSI), and fruit firmness (FN), but did not significantly affect fruit water content (FWC). The DDITs at stages III and IV significantly enhanced total soluble solids (TSS), soluble sugars (SS), and vitamin C (V c) in 2021 and 2022, but a notable decrease in titratable acidity (TA) was observed. DDITs at stage IV exhibited the highest comprehensive benefit of apple, followed by DDITs at stages I and II, and DDITs at stage III had the lowest values. The DDITs of D15%, D30%, CK, and D15% at stages I, II, III, and IV were identified as the optimal irrigation strategy to improve apple yield, quality, and WP. Our findings can guide the development of appropriate irrigation strategies for apple cultivation in Loess Plateau of China, promoting efficient and sustainable apple production. • Deficit drip irrigation treatments (DDITs) greatly decreased midday leaf water potential. • Proper DDITs at fruit maturation stage improved apples physical/ chemical quality. • Proper DDITs during the early growth stage greatly improved WP without yield loss. • The optimal irrigation strategy was determined using the fuzzy Borda combined model. [ABSTRACT FROM AUTHOR]

The synergistic effects between Co NPs and pyridinic nitrogen of Co/CN-x catalysts was the key to realize the base-free oxidation of mercaptans to their corresponding disulfides by decreasing the energy barrier for the deprotonation of mercaptans. [Display omitted] The oxidation of mercaptans under mild and base-free conditions is of vital importance in terms of economy and environment for petroleum processing industry. Here, we developed a series of MOF-derived cobalt-based nitrogen-doped (N -doped) carbon (Co/CN-x) catalysts for the base-free catalytic oxidation of mercaptans. The optimal Co/CN-900 showed excellent catalytic activity for the oxidation of mercaptans under base-free conditions, yielding complete conversion of various mercaptans and > 99.0% selectivity of disulfides. The high performance can be contributed to the advantages of hierarchical pore structure for the diffusion and migration of substrates, self-carrying alkalinity for the formation of mercaptide anion, abundant active Co sites for catalytic oxidation of mercaptans as well as the synergistic effects between the Co nanoparticles (NPs) and N -doped carbon supports. Furthermore, a possible mechanism for base-free catalytic oxidation of mercaptans over Co/CN-x catalysts is proposed based on a set of control experiments and density functional theory (DFT) calculations. [ABSTRACT FROM AUTHOR]

The great challenges of food security, climate change and environmental degradation resulting from unreasonable nitrogen fertilizer (NF) inputs necessitate the optimization of fertilizer application to ensure fruit production and environmental sustainability. However, there is a lack of systematic analysis on the effects of NF inputs on apple yield and reactive nitrogen losses (N 2 O emission, NH 3 volatilization, and nitrate leaching) at the global scale. Therefore, we used a meta-analysis of 159 observations from 31 published studies to evaluate the response of apple yield and reactive nitrogen losses to NF. We aim to identify suitable NF rates, environmental conditions, and planting factors that improve apple yield while minimizing reactive nitrogen losses. Our results showed that NF significantly increased apple yield, N 2 O emission, NH 3 volatilization, and nitrate leaching by 17.1%, 255.7%, 236.4%, and 68.7% compared with no nitrogen fertilizer (NNF), respectively. The effects of NF rates and environmental factors on the response of N 2 O emission, NH 3 volatilization, and nitrate leaching to NF were prior to those of planting factors, but the result of apple yield was the opposite. Apple production under NF in regions with MAT (mean annual air temperature) ≥ 10 °C and MAP (mean annual precipitation) < 600 mm declined the risk of reactive nitrogen losses and significantly increased apple yield by 24.4%−28%. NF application was more beneficial in soil with pH ≥ 7 and SOM < 15 g kg−1 for apple production. We found that the suitable NF rate was 250–450 kg ha−1, which can increase apple yield by 0.8%−21.3% while significantly reducing reactive nitrogen losses compared with lower NF rate (< 250 kg ha−1) and higher NF rate (> 450 kg ha−1). Our findings provide guidance for optimizing NF management in apple orchard to achieve high crop yields, reduce emissions, and mitigate pollution. • Nitrogen (N) fertilizer significantly boosted apple yield and reactive nitrogen losses. • N fertilizer application was more beneficial in soil with pH ≥ 7 and SOM < 15 g kg−1. • Recommended N fertilizer rate was 250–450 kg ha−1, which can increase apple yield while minimizing reactive nitrogen losses. [ABSTRACT FROM AUTHOR]

Deficit irrigation (DI) is widely recognized as an irrigation method to save water and increase/maintain yield. The objective of this study was to evaluate the effects of DI on yield and water productivity (WP, the ratio of yield divided by evapotranspiration (ET)) of woody fruits (apple, citrus, pear, peach), herbaceous fruits (strawberry, watermelon) and vine fruit (grape), and to identify the optimal irrigation management strategy for different fruit species groups. For this, we conducted a comprehensive meta-analysis with 591 observations from 56 peer-reviewed papers. Results showed that DI reduced the yield of woody, herbaceous, and vine fruits by 13.74%, 20.51%, and 9.03%, and increased WP by 13.34%, − 2.08%, and 9.89% compared with full irrigation (FI), respectively. Herbaceous fruits were more vulnerable to yield reduction than woody and vine fruits under DI. As for woody fruits, compared with FI, low degree (80%−100% irrigation amount of FI) DI performed better, increasing yield and WP by 0.87% and 9.77%. Woody fruits are suitable for DI in stage I and stage II (bud burst to leafing stage and flowering to fruit set stage), which can reduce the risk of yield reduction and significantly increase WP by 1.86%− 9.28%. Among irrigation methods, surge-root irrigation and sprinkler irrigation under DI performed better for woody fruits, increasing yield and WP by 1.81% and 11.89%, − 5.85% and 43.91%, respectively. In terms of herbaceous fruits, compared with FI, mild degree (60%−80% FI) DI declined the risk of yield reduction and significantly increased WP by 2.25%. DI at stage IV (fruit maturation stage) performed better, which can decrease the risk of herbaceous fruit yield reduction and improve WP by 0.37%. Among irrigation methods, furrow irrigation under DI performed better for herbaceous fruits, increasing yield and WP by − 0.66% and 2.29%. In terms of vine fruits, compared with FI, moderate degree (40%−60% FI) DI performed better, which can significantly increase yield and WP by − 8.05% and 13.87%. Vine fruits are suitable for DI in stage I, increasing yield and WP by 5.38% and 22.13%. For woody fruits, DI is suitable for higher seasonal precipitation (SP > 400 mm) and annual average temperature (AAT ≥ 10 °C). In contrast, for vine fruits and herbaceous fruits, DI is suitable for lower SP (< 200 mm) and AAT (< 10 °C). Our findings provide guidance for precise water deficit management of woody, herbaceous, and vine fruits. • As for vine fruits, moderate degree DI (deficit irrigation) at stage I (bud burst to leafing stage) was recommended. • As for woody fruits, low degree DI at stage I or II (flowering to fruit set stage) performed better. • As for herbaceous fruits, mild degree DI at stage IV (fruit maturation stage) can save water and increase yield. [ABSTRACT FROM AUTHOR]

Accurate prediction of global solar radiation (R s) is vital for investment decisions and solar energy distribution. In this study, three hybrid models (ACO-SVM, CS-SVM, and GWO-SVM) based on ant colony optimization (ACO), cuckoo search (CS) and grey wolf optimization (GWO) algorithms were proposed to optimize support vector machine (SVM) for predicting R s in four climate zones of China (temperate continental zone TCZ, mountain plateau zone MPZ, temperate monsoon zone TMZ, and subtropical monsoon zone SMZ). They were compared with the standalone backpropagation neural network model, decision tree, and support vector machines. The results demonstrated that among the standalone models, support vector machines performed best with the highest accuracy in R s estimation in each climate zone of China, followed by the decision tree and backpropagation neural network models, with a coefficient of determination (R2) in 0.707–0.882, 0.694–0.881, and 0.681–0.850, respectively. In contrast, the hybrid models exhibited higher accuracy than standalone support vector machines in four climatic regions of China, with the coefficient of determination (R2) increasing by 5.361%, 5.476%, 7.382%, and 10.965%, respectively. Among hybrid models, GWO-SVM performed better than CS-SVM, and both had higher accuracy than ACO-SVM, with the coefficient of determination (R2) in 0.809–0.927, 0.804–0.926, and 0.793–0.930, respectively. Therefore, the hybrid models (ACO-SVM, CS-SVM, and GWO-SVM), especially GWO-SVM and CS-SVM, can significantly improve the accuracy for predicting R s in various regions of China. •For standalone models, the SVM had higher prediction accuracy than DT and BP. •Three hybrid models (ACO-SVM, CS-SVM and GWO-SVM) were proposed to predict daily R s in different climatic zones of China. •The accuracy of hybrid models was higher than the standalone SVM model. •The GWO-SVM model performed better than the CS-SVM model, and the accuracy of both were better than ACO-SVM model. [ABSTRACT FROM AUTHOR]

• Highly flexible CNTs based monolithic support was constructed via a directional freezing method. • Monolithic CNTs based composite PCM exhibits superior mechanical and thermal properties. • Flexible mechanism of monolithic CNTs based composite PCM is proposed. • This design strategy provides a new direction for future wearable fitting-skin temperature-controlled materials. Currently, most reported composite phase change materials (PCMs) are powdery shape, which require secondary processing for practical applications. Although some monolithic composite PCMs have been developed, their flexibility usually undergoes a remarkable reduction or even complete disappearance when supporting material s are infiltrated with PCMs. To solve this problem, we fabricated a flexible supporting material with a folded layer-bridge network structure by dispersing carbon nanotubes (CNTs) in acetic acid solution of chitosan (CS) with poly(vinyl alcohol) (PVA) using a directional freezing method. Then CS/PVA/CNTs (CPC) scaffold was infiltrated with polyethylene glycol (PEG) to prepare PEG@CPC composite PCM. The resulting flexible composite PCM displays excellent mechanical properties, such as high tensile strength of 2.42 MPa and bending resistance of >100 cycles. Moreover, it displays outstanding thermal properties, such as high crystallinity of close to 100% and encapsulation ratio of 92.6 wt%. This work provides a simple method for preparation of flexible monolithic composite PCMs for many potential applications, such as wearable fitting-skin temperature-controlled materials. [ABSTRACT FROM AUTHOR]