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Precise nitrogen supply is crucial for ensuring the quality of cut chrysanthemums (Chrysanthemum morifolium Ramat.). The nitrogen nutrition index (NNI) serves as an important indicator for diagnosing crop nitrogen (N) nutrition. Hyperspectral remote sensing (HRS) technology has been widely used in monitoring crop N status due to its rapid, accurate, and non-destructive capabilities. However, its application in estimating the NNI of cut chrysanthemums has received limited attention. Therefore, this study aimed to use HRS to accurately determine the cut chrysanthemum NNI, thereby providing valuable guidance for managing N fertilization. During several key growth stages, a hyperspectral spectroradiometer was used to capture hyperspectral reflectance data (350–2500 nm) from three leaf layers. Subsequently, cut chrysanthemum canopies were sampled for aboveground biomass (AGB) and plant nitrogen concentration (PNC). The collected AGB and PNC data were then utilized to fit the critical N (Nc) dilution curve of cut chrysanthemums using a Bayesian hierarchical model, enabling the calculation of the NNI. Finally, spectral indices and partial least squares regression (PLSR) were used to establish the NNI estimation model for cut chrysanthemums. The results showed that the Nc dilution curve of the cut chrysanthemums was Nc = 5.401 × AGB−0.468. The first leaf layer (L1) proved to be optimal for estimating cut chrysanthemum NNI. Additionally, a newly proposed two-band spectral index, DVI-L1 (R1105, R700), demonstrated moderate predictive capabilities for the NNI of cut chrysanthemums (R2 = 0.5309, RMSE = 0.3210). Compared with the spectral index-based NNI estimation model, PLSR-L1 showed the best performance in estimating the cut chrysanthemum NNI (R2 = 0.8177, RMSE = 0.2000). Our results highlight the rapid NNI prediction potential of HRS and its significance in facilitating precise N management in cut chrysanthemums.

Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results revealed that the Mask R-CNN exhibited the best performance in crown diameter estimation (sample number = 2409, R2 = 0.9629, RMSE = 2.2949 cm). Following closely, the HSV color space-based model exhibited strong performance (sample number = 2409, R2 = 0.9465, RMSE = 3.4073 cm). Both of the first two methods were efficient in estimating crown diameter throughout the entire growth stage. In contrast, the RGB color space-based model exhibited slightly lower performance (sample number = 1065, R2 = 0.9011, RMSE = 3.3418 cm) and was only applicable during periods when the entire plant was predominantly green. These findings provide theoretical and technical support for utilizing UAV-based imagery to estimate the crown diameter of garden chrysanthemums.

Plant potassium accumulation (PKA) plays an important role in evaluating the production capacity of crops. Remote sensing from unmanned aerial vehicles (UAVs) is increasingly used in precision agriculture, but relatively little is known about its use for determining the potassium (K) nutritional status of rice (Oryza sativa L.). This study compares the performance of different sets of spectral and textural indices derived from UAV data for estimating rice K nutritional status. A UAV equipped with three cameras (RGB, color-infrared (CIR) and multispectral cameras) was used to acquire imagery of rice canopies at different key growth stages. Immediately following the overflights, rice canopies were sampled for rice PKA. Regression models were then built to predict rice PKA with spectral and textural indices as predictor variables. Finally, stepwise multiple linear regression (SMLR) was used to determine if fusing spectral and textural indices significantly improved UAV-based PKA estimates. The renormalized difference vegetation index (RDVI) calculated from multispectral imagery proved to be the best predictor of rice PKA (R2 = 0.72, RMSE = 5.42 g m−2), though spectral vegetation indices calculated from RGB and CIR imagery also showed moderately strong prediction capability (R2 ≤ 0.56, RMSE ≥ 7.33 g m−2). Among the textural indices, the renormalized difference texture index [RDTI (MEA800, MEA680)] calculated by mean texture (MEA) of the multispectral imagery was the best predictor of rice PKA (R2 = 0.74, RMSE = 5.57 g m−2) while textural indices calculated from the RGB and CIR imagery showed only weak relationships with rice PKA (R2 ≤ 0.40, RMSE ≥ 8.11 g m−2). Fusing textural and spectral vegetation indices improved our ability to remotely sense PKA, with the SMLR model combination of RDTI (MEA800, MEA680) and spectral vegetation index DATT performing best (R2 increased by 11.11% to 0.80 and RMSE decreased by 7.5% to 5.15 g m−2). Our findings suggest that spectral and textural indices derived from UAV data allow for accurate mapping of PKA and that fusion of the two further improves the accuracy.

Timely and accurate prediction of crop yield prior to harvest is vital for precise agricultural management. Unmanned aerial vehicles (UAVs) provide a fast and convenient approach to crop yield prediction, but most existing crop yield models have rarely been tested across different years, cultivars and sensors. This has limited the ability of these yield models to be transferred to other years or regions or to be potentially used with data from other sensors. In this study, UAV-based multispectral imagery was used to predict rice grain yield at the booting and filling stages from four field experiments, involving three years, two rice cultivars, and two UAV sensors. Reflectance and texture features were extracted from the UAV imagery, and vegetation indices (VIs) and normalized difference texture indices (NDTIs) were computed. The models were independently validated to test the stability and transferability across years, rice cultivars, and sensors. The results showed that the red edge normalized difference texture index (RENDTI) was superior to other texture indices and vegetation indices for model regression with grain yield in most cases. However, the green normalized difference texture index (GNDTI) achieved the highest prediction accuracy in model validation across rice cultivars and sensors. The yield prediction model of Japonica rice achieved stronger transferability to Indica rice with root mean square error (RMSE), bias, and relative RMSE (RRMSE) of 1.16 t/ha, 0.08, and 11.04%, respectively. Model transferability was improved significantly between different sensors after band correction with a decrease of 15.05–59.99% in RRMSE. Random forest (RF) was found to be a good solution to improve the model transferability across different years and cultivars and obtained the highest prediction accuracy with RMSE, bias, and RRMSE of 0.94 t/ha, −0.21, and 9.37%, respectively. This study provides a valuable reference for crop yield prediction when existing models are transferred across different years, cultivars and sensors.

This work explored potassium nutrient retrieval in wheat blades using reflectance spectra. Spectral data were collected from wheat blades at different growth stages, in different cultivars, and following different fertilisation treatments from 2016 to 2019 using a leaf clip and halogen bulb with an ASD spectrometer. Reflectance data from 350 to 2500 nm were collected, and data of 400 to 2400 nm were used in the retrieval. Using a leaf clip to measure the reflectance of a narrow blade can cause bias, which can be corrected using a normalisation method, i.e. the reflectance of each band was divided by the average reflectance of all bands. Three such methods were employed: vegetation index (VI), partial least squares (PLS), and random forest (RF). The approach yielded leaf potassium content (LKC, %) and leaf potassium per area (LKA, g/m2). The results showed that newly developed VIs outperformed previously published indices. The model using a modified ratio spectral index, mRSI(2275, 1875), yielded LKC with a coefficient of determination (R2) of 0.61 and a root mean square error (RMSE) of 0.57%. Normalisation methods can eliminate multiplicative error in blade spectra, thereby correcting the underestimated reflectance of narrow blades, and improving the accuracy of potassium retrieval models. Among the three methods, PLS achieved the highest accuracy. The retrieval of LKC and LKA based on normalised spectra and the PLS method yielded R2 values of 0.74 and 0.65, respectively, and their corresponding RMSE values were 0.46% and 0.21 g/m2. LKC retrieval models had higher R2 values than LKA models. This comprehensive analysis of different methods revealed the importance of reflectance at 1883 nm and 2305 nm. In conclusion, it is feasible to retrieve wheat leaf potassium levels using spectral data.

Potassium (K) plays a significant role in the formation of crop quality and yield. Accurate estimation of plant potassium content using remote sensing (RS) techniques is therefore of great interest to better manage crop K nutrition. To improve RS of crop K, meteorological information might prove useful, as it is well established that weather conditions affect crop K uptake. We aimed to determine whether including meteorological data into RS-based models can improve K estimation accuracy in rice (Oryza sativa L.). We conducted field experiments throughout three growing seasons (2017–2019). During each year, different treatments (i.e., nitrogen, potassium levels and plant varieties) were applied and spectra were taken at different growth stages throughout the growing season. Firstly, we conducted a correlation analysis between rice plant potassium content and transformed spectra (reflectance spectra (R), first derivative spectra (FD) and reciprocal logarithm-transformed spectra (log [1/R])) to select correlation bands. Then, we performed the genetic algorithms partial least-squares and linear mixed effects model to select important bands (IBs) and important meteorological factors (IFs) from correlation bands and meteorological data (daily average temperature, humidity, etc.), respectively. Finally, we used the spectral index and machine learning methods (partial least-squares regression (PLSR) and random forest (RF)) to construct rice plant potassium content estimation models based on transformed spectra, transformed spectra + IFs and IBs, and IBs + IFs, respectively. Results showed that normalized difference spectral index (NDSI (R1210, R1105)) had a moderate estimation accuracy for rice plant potassium content (R2 = 0.51; RMSE = 0.49%) and PLSR (FD-IBs) (R2 = 0.69; RMSE = 0.37%) and RF (FD-IBs) (R2 = 0.71; RMSE = 0.40%) models based on FD could improve the prediction accuracy. Among the meteorological factors, daily average temperature contributed the most to estimating rice plant potassium content, followed by daily average humidity. The estimation accuracy of the optimal rice plant potassium content models was improved by adding meteorological factors into the three RS models, with model R2 increasing to 0.65, 0.74, and 0.76, and RMSEs decreasing to 0.42%, 0.35%, and 0.37%, respectively, suggesting that including meteorological data can improve our ability to remotely sense plant potassium content in rice.

The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720−740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

Accurate estimation of the nitrogen (N) spatial distribution of rice (Oryza sativa L.) is imperative when it is sought to maintain regional and global carbon balances. We systematically evaluated the normalized differences of the soil and plant analysis development (SPAD) index (the normalized difference SPAD indexes, NDSIs) between the upper (the first and second leaves from the top), and lower (the third and fourth leaves from the top) leaves of Japonica rice. Four multi-location, multi-N rate (0⁻390 kg ha−1) field experiments were conducted using seven Japonica rice cultivars (9915, 27123, Wuxiangjing14, Wunyunjing19, Wunyunjing24, Liangyou9, and Yongyou8). Growth analyses were performed at different growth stages ranging from tillering (TI) to the ripening period (RP). We measured leaf N concentration (LNC), the N nutrition index (NNI), the NDSI, and rice grain yield at maturity. The relationships among the NDSI, LNC, and NNI at different growth stages showed that the NDSI values of the third and fourth fully expanded leaves more reliably reflected the N nutritional status than those of the first and second fully expanded leaves (LNC: NDSIL3,4, R2 > 0.81; NDSIothers, 0.77 > R2 > 0.06; NNI: NDSIL3,4, R2 > 0.83; NDSIothers, 0.76 > R2 > 0.07; all p < 0.01). Two new diagnostic models based on the NDSIL3,4 (from the tillering to the ripening period) can be used for effective diagnosis of the LNC and NNI, which exhibited reasonable distributions of residuals (LNC: relative root mean square error (RRMSE) = 0.0683; NNI: RRMSE = 0.0688; p < 0.01). The relationship between grain yield, predicted yield, and NDSIL3,4 were established during critical growth stages (from the stem elongation to the heading stages; R2 = 0.53, p < 0.01, RRMSE = 0.106). An NDSIL3,4 high-yield change curve was drawn to describe critical NDSIL3,4 values for a high-yield target (10.28 t ha−1). Furthermore, dynamic-critical curve models based on the NDSIL3,4 allowed a precise description of rice N status, facilitating the timing of fertilization decisions to optimize yields in the intensive rice cropping systems of eastern China.

Abstract Electrocatalysis holds the key to enhancing the efficiency and cost-effectiveness of water splitting devices, thereby contributing to the advancement of hydrogen as a clean, sustainable energy carrier. This study focuses on the rational design of Ru nanoparticle catalysts supported on TiN (Ru NPs/TiN) for the hydrogen evolution reaction in alkaline conditions. The as designed catalysts exhibit a high mass activity of 20 A mg−1 Ru at an overpotential of 63 mV and long-term stability, surpassing the present benchmarks for commercial electrolyzers. Structural analysis highlights the effective modification of the Ru nanoparticle properties by the TiN substrate, while density functional theory calculations indicate strong adhesion of Ru particles to TiN substrates and advantageous modulation of hydrogen adsorption energies via particle-support interactions. Finally, we assemble an anion exchange membrane electrolyzer using the Ru NPs/TiN as the hydrogen evolution reaction catalyst, which operates at 5 A cm−2 for more than 1000 h with negligible degradation, exceeding the performance requirements for commercial electrolyzers. Our findings contribute to the design of efficient catalysts for water splitting by exploiting particle-support interactions.

Abstract The buried interface between the electron transport layer (ETL) and the perovskite layer plays a crucial role in enhancing the power conversion efficiency (PCE) and stability of n–i–p type perovskite solar cells (PSCs). In this study, the interface between the chemical bath deposited (CBD) titanium oxide (TiO2) ETL and the perovskite layer using multi‐functional potassium trifluoromethyl sulfonate (SK) is modified. Structural and elemental analyses reveal that the trifluoromethyl sulfonate serves as a crosslinker between the TiO2 and the perovskite layer, thus improving the adhesion of the perovskite to the TiO2 ETL through strong bonding of the ─CF3 and ─SO3− terminal groups. Furthermore, the multi‐functional modifiers reduced interface defects and suppressed carrier recombination in the PSCs. Consequently, devices with a champion PCE of 25.22% and a fill factor (FF) close to 85% is achieved, marking the highest PCE and FF observed for PSCs based on CBD TiO2. The unencapsulated device maintained 81.3% of its initial PCE after operating for 1000 h.

Abstract Cuprous oxide (Cu2O) is a promising oxide material for photoelectrochemical water splitting (PEC), and increasing its photovoltage is the key to creating efficient overall PEC water-splitting devices. Previous reports are mostly focused on optimizing the energy band alignment between Cu2O and the n-type buffer layer to improve the photovoltage of Cu2O photocathodes. However, the band alignment between the n-type buffer layer and the protective layer is often ignored. In this work, Cu2O photocathodes with a single buffer layer (Ga2O3) and dual buffer layers (Ga2O3/ZnGeOx) are fabricated, and their PEC performances are compared. Results show that after inserting the second buffer layer (ZnGeOx), the onset potential of the Cu2O photocathode increases by 0.16 V. Operando electrochemical impedance spectroscopy measurements and analysis of the energy-level diagrams of each layer show that an energy level gradient between Ga2O3 and TiO2 is created when ZnGeOx is introduced, which eliminates the potential barrier at the interface of Ga2O3/TiO2 and improves the photovoltage of the Cu2O photocathode. Our work provides an effective approach to improve the photovoltage of photoelectrodes for solar water splitting by introducing dual buffer layers.

Electrochemical CO2 reduction driven by renewable electricity is one of the promising strategies to store sustainable energy as fuels. However, the selectivity of value-added multi-carbon products remains poor for further application of this process. Here, we regulate CO adsorption by forming a Nafion layer on the copper (Cu) electrode that is repulsive to OH−, contributing to enhanced selectivity of CO2 reduction to C2+ products with the suppression of C1 products. The operando Raman spectroscopy indicates that the local OH− would adsorb on part of active sites and decrease the adsorption of CO. Therefore, the electrode with repulsive to OH− can adjust the concentration of OH−, leading to the increased adsorption of CO and enhanced C–C coupling. This work shows that electrode design could be an effective strategy for improving the selectivity of CO2 reduction to multi-carbon products.

Abstract Halide perovskites solar cells are now approaching commercialisation. In this transition from academic research towards industrialisation, standardized testing protocols and reliable dissemination of performance metrics are crucial. In this study, we analyze data from over 16,000 publications in the Perovskite Database to investigate the assumed equality between the integrated external quantum efficiency and the short circuit current from JV measurements. We find a systematic discrepancy with the JV-values being on average 4% larger. This discrepancy persists across time, perovskite composition, and device architecture, indicating the need to explore new perovskite physics and update reporting protocols and assumptions in the field.

Diabetic neuropathic pain (DNP) is a common complication of diabetes characterized by persistent pain. Emerging evidence links astrocytes to mechanical nociceptive processing, and the motor cortex (MCx) is a cerebral cortex region that is known to play a key role in pain regulation. However, the association between MCx astrocytes and DNP pathogenesis remains largely unexplored. Here, we studied this association using designer receptors exclusively activated by designer drugs to specifically manipulate MCx astrocytes. We proved that the selective inhibition of MCx astrocytes reduced DNP in streptozocin (STZ)- induced DNP models and discovered a potential mechanism by which astrocytes release cytokines, including TNF-a and IL-1b, to increase neuronal activation in the MCx, thereby regulating pain. Together, these results demonstrate a pivotal role for MCx astrocytes in DNP pathogenesis and provide new insight into DNP treatment strategies. [ABSTRACT FROM AUTHOR]

Abstract During the last decade lead halide perovskites have shown great potential for photovoltaic applications. However, the stability of perovskite solar cells still restricts commercialization, and lack of properly implemented unified stability testing and disseminating standards makes it difficult to compare historical stability data for evaluating promising routes towards better device stability. Here, we propose a single indicator to describe device stability that normalizes the stability results with respect to different environmental stress conditions which enables a direct comparison of different stability results. Based on this indicator and an open dataset of heterogeneous stability data of over 7000 devices, we have conducted a statistical analysis to assess the effect of different stability improvement strategies. This provides important insights for achieving more stable perovskite solar cells and we also provide suggestions for future directions in the perovskite solar cell field based on big data utilization.

The efficient utilization of carbon dioxide (CO2) as a resource, comprises three key processes: CO2 capture, catalytic conversion and product purification. Using the renewable electricity to drive these processes provides a promising pathway for mitigating the ever-increasing atmospheric CO2 concentration whilst simultaneously addressing the growing energy demand. Although each of the three individual processes has been extensively investigated during the past decade, the rapid and economically viable reduction of CO2 emissions still calls for the development of an integrated electrochemical system driven by the renewable electricity to achieve carbon neutrality. Herein, we report a systematic protocol to bridge the three individual CO2 utilization processes into one coupled electrochemical system: a bipolar membrane electrodialysis (BPMED) cell generating alkaline and acidic solutions for the capture and recovery of CO2, a flow cell with an Ag gas diffusion electrode (GDE) for the selective electrocatalytic reduction of the recovered CO2, and an alkaline solution container for the purification of the gaseous products and recycle of the unreacted CO2. Consequently, the coupled electrochemical system successfully captured CO2 from the simulated flue gas and converted it into a pure syngas stream.

Pain triggers emotional changes in humans and animals, including fear and anxiety. Conversely, fear and anxiety may enhance suffering of patients with pain. However, in animal models of acute pain, it has been reported that fear may inhibit pain by activating endogenous inhibitory systems. In this study, we wanted to examine if behavioral withdrawal responses may be affected during fear retrieval, a condition where fear-associated tone is applied. We found that thermal pain thresholds were significantly increased during fear retrieval. Our results indicate that animals are suffering fear like-events, while their behavioral responses are inhibited. These results indicate that it will be important to evaluate both emotional and behavioral withdrawal responses for future development of new pain medicine. [ABSTRACT FROM AUTHOR]

Summary: The cement industry is one of the largest contributors to global CO2 emissions, which has been paid more attention to the research on converting the CO2 released by the cement production process. It is extremely challenging to decarbonize the cement industry, as most CO2 emissions result from the calcination of limestone (CaCO3) into CaO and CO2. In this work, we demonstrate an in situ electrochemical process that transforms CaCO3 into portlandite (Ca(OH)2, a key Portland cement precursor) and valuable carbonaceous products, which integrates electrochemical water splitting and CO2 reduction reaction with the chemical decomposition of CaCO3. With different metal catalyst electrodes (like Au, Ag, In, Cu, and Cu nanowires electrodes), we have achieved various valuable carbonaceous products, such as CO, formate, methane, ethylene, and ethane during the electrochemical CO2 process. Our work demonstrates a proof of concept for green and sustainable cement production.

Abstract Perovskite solar cells (PSCs) have attracted intense attention based on their high power conversion efficiency and low production cost. However, due to the polycrystalline nature and the intrinsic hydrophilicity of the metal halide perovskite moieties, the photovoltaic performance of PSCs is largely limited by defects within the polycrystalline perovskites and the sensitivity to moisture. In this perspective, we focus on the chemically tailored interface materials to passivate the defects and improve the moisture stability of PSCs. First, we provide a brief overview of various molecular interface modifiers. Thereafter we provide examples from our recent work on organic ammonium halide‐based passivation materials as representatives to illustrate the design strategies and the modification effects. In the end, we shed light on the future development of organic ammonium halides for applications in PSCs.

Parkinson’s disease (PD) is the second most common neurodegenerative disease globally, and there is currently no effective treatment for this condition. Excessive accumulation of reactive oxygen species (ROS) and neuroinflammation are major contributors to PD pathogenesis. Herein, ultrasmall nanoscale coordination polymers (NCPs) coordinated by ferric ions and natural product curcumin (Cur) were exploited, showing efficient neuroprotection by scavenging excessive radicals and suppressing neuroinflammation. In a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mouse PD model, such ultrasmall Fe-Cur NCPs with prolonged blood circulation and BBB traversing capability could effectively alleviate oxidative stress, mitochondrial dysfunction, and inflammatory condition in the midbrain and striatum to reduce PD symptoms. Thus, this study puts forth a unique type of therapeutics-based NCPs that could be used for safe and efficient treatment of PD with potential in clinical translation.

Abstract Cell membrane coating technology is an approach to the biomimetic replication of cell membrane properties, and is an active area of ongoing research readily applicable to nanoscale biomedicine. Nanoparticles (NPs) coated with cell membranes offer an opportunity to unite natural cell membrane properties with those of the artificial inner core material. The coated NPs not only increase their biocompatibility but also achieve effective and extended circulation in vivo, allowing for the execution of targeted functions. Although cell membrane-coated NPs offer clear advantages, much work remains before they can be applied in clinical practice. In this review, we first provide a comprehensive overview of the theory of cell membrane coating technology, followed by a summary of the existing preparation and characterization techniques. Next, we focus on the functions and applications of various cell membrane types. In addition, we collate model drugs used in cell membrane coating technology, and review the patent applications related to this technology from the past 10 years. Finally, we survey future challenges and trends pertaining to this technology in an effort to provide a comprehensive overview of the future development of cell membrane coating technology.

Element doping has been proven a useful strategy to tune the properties of halide perovskites. Here Xiang et al. show that barium unexpectedly does not incorporate in perovskite lattice but induces phase segregation and bandgap reduction and inhibits non-radiative recombination.

Various approaches have been developed to push higher the efficiency of halide perovskite solar cells. Here Alharbi et al. show that ammonium salts treatment can reduce the defect density at the perovskite surface and understand the passivation mechanism with 2D-solid state NMR.

Mitochondrial dysfunction is commonly detected in individuals suffering from Parkinson’s disease (PD), presenting within the form of excessive reactive oxygen species (ROS) generation as well as energy metabolism. Overcoming this dysfunction within brain tissues is an effective approach to treat PD, while unluckily, the blood-brain barrier (BBB) substantially impedes intracerebral drug delivery. In an effort to improve the delivery of efficacious therapeutic drugs to the brain, a drug delivery platform hydrogel (MAG-NCs@Gel) was designed by complexing magnolol (MAG)-nanocrystals (MAG-NCs) into the noninvasive thermosensitive poly(N-isopropylacrylamide) (PNIPAM) with self-gelation. The as-prepared MAG-NCs@Gel exhibited obvious improvements in drug solubility, the duration of residence with the nasal cavity, and the efficiency of brain targeting, respectively. Above all, continuous intranasal MAG-NCs@Gel delivery enabled MAG to cross the BBB and enter dopaminergic neurons, thereby effectively alleviating the symptoms of MPTP-induced PD. Taking advantage of the lower critical solution temperature (LCST) behavior of this delivery platform increases its viscoelasticity in nasal cavity, thus improving the efficiency of MAG-NCs transit across the BBB. As such, MAG-NCs@Gel represented an effective delivery platform capable of normalizing ROS and adenosine triphosphate (ATP) in the mitochondria of dopaminergic neurons, consequently reversing the mitochondrial dysfunction and enhancing the behavioral skills of PD mice without adversely affecting normal tissues.

Engineering hybrid perovskites at the molecular level to solve the stability problem remains a challenge. Here Grätzel et al. design a multifunctional molecular modulator that interacts with the perovskite via modes elucidated by solid state NMR spectroscopy and show high efficiency and operational stability.

Inadequate pore infiltration and low conductivity of hole transporter materials limit the performance of solid-state dye-sensitized solar cells. Using fast charge-exchange Cu(II/I) complexes as part of the hole transporting material, Caoet al. overcome these issues to achieve a record photoconversion efficiency of 11%.

Abstract Intensive studies on low‐temperature deposited electron transport materials have been performed to improve the efficiency of n‐i‐p type planar perovskite solar cells to extend their application on plastic and multijunction device architectures. Here, a TiO2 film with enhanced conductivity and tailored band edge is prepared by magnetron sputtering at room temperature by hydrogen doping (HTO), which accelerates the electron extraction from perovskite photoabsorber and reduces charge transfer resistance, resulting in an improved short circuit current density and fill factor. The HTO film with upward shifted Fermi level guarantees a smaller loss on VOC and facilitates the growth of high‐quality absorber with much larger grains and more uniform size, leading to devices with negligible hysteresis. In comparison with the pristine TiO2 prepared without hydrogen doping, the HTO‐based device exhibits a substantial performance enhancement leading to an efficiency of 19.30% and more stabilized photovoltaic performance maintaining 93% of its initial value after 300 min continuous illumination in the glove box. These properties permit the room‐temperature magnetron sputtered HTO film as a promising electron transport material for flexible and tandem perovskite solar cell in the future.