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High-entropy materials consisting of disordered multiple components can exhibit enhanced materials properties compared with their individual constituents. Although various high-entropy materials have been developed based on the configurational disorder of mixed inorganic components, the potential of organic moieties for high-entropy structures remains underexplored. Here we report a family of high-entropy organic–inorganic hybrid perovskites for photovoltaic applications. By mixing different A-site organic cations with various alkyl chains, we obtain a hybrid crystal structure with ordered inorganic frameworks and disordered organic moieties, leading to increased entropy. The hybrid perovskite exhibits superior properties compared with its single-component counterpart, including increased resilience to structural transitions and heat stress. When used in solar cells, the high-entropy hybrid perovskite leads to devices with a power conversion efficiency of 25.7% (certified, 25.5%) for an inverted-cell architecture. Cells retain over 98% of their initial power conversion efficiency after 1,000 h of operation under continuous illumination (AM 1.5 G), with a linear extrapolation to the T90value of 5,040 h. In particular, the structural disorder of this class of high-entropy materials can also reduce non-radiative recombinations for a wide range of perovskite composition, stoichiometry deviation, film-processing history and device architecture. This universal and error-tolerant strategy can, thus, benefit the production yield of perovskite solar cells in future industrial mass production. Given the rich chemistry of organic moieties and mixing configuration, this work may also open up more opportunities to tune the stability and optoelectronic properties of perovskite materials for photoelectric applications.

Previous findings have suggested a close association between oxygen vacancies in SnO2and charge carrier recombination as well as perovskite decomposition at the perovskite/SnO2interface. Underlying the fundamental mechanism holds great significance in achieving a more favorable balance between the efficiency and stability. In this study, we prepared three SnO2samples with different oxygen vacancy concentrations and observed that a low oxygen vacancy concentration is conducive to long-term device stability. Iodide ions were observed to easily diffuse into regions with high oxygen vacancies, thereby speeding up the deprotonation of FAI, as made evident by the detection of the decomposition product formamide. In contrast, a high oxygen vacancy concentration in SnO2could prevent hole injection, leading to a decrease in interfacial recombination losses. To suppress this decomposition reaction and address the trade-off, we designed a bilayer SnO2structure to ensure highly efficient carrier transport still while maintaining a chemically inert surface. As a result, an enhanced efficiency of 25.06% (certified at 24.55% with an active area of 0.09 cm2under fast scan) was achieved, and the extended operational stability maintained 90% of their original efficiency (24.52%) after continuous operation for nearly 2000 h. Additionally, perovskite submodules with an active area of 14 cm2were successfully assembled with a PCE of up to 22.96% (20.09% with an aperture area).

Organic magnesium storage materials are intriguing as cathode materials for rechargeable magnesium batteries (RMBs) because of their flexible structure and the potential of fast Mg-ion diffusion kinetics. However, the dissolution of these materials in the electrolytes of the RMBs limits their application. Herein, the pentacenetetrone (PT) and its polymer poly(pentacenetetrone sulfide) (PPTS) are developed as cathode materials for RMBs. Given the benefit of its insoluble property in the Mg-ion electrolyte, the as-prepared PPTS cathode exhibits significantly enhanced reversible capacity, coulombic efficiency, and rate performance compared to PT. For instance, the high capacities of 241 mAh g–1(100 mA g–1) and 131 mAh g–1(2 A g–1) are obtained for the PPTS cathode, demonstrating a competitive rate performance of PPTS compared to previously reported organic cathodes of RMBs. In contrast, the soluble PT cathode exhibits much lower capacities of 163 mAh g–1(100 mAh g–1) and 31 mAh g–1(2 A g–1).

The coplanar symmetric small molecules-assisted CsPbI3crystallization enables the achievement of high-quality perovskite films, which can modulate energy bands and facilitate efficient carrier extraction, resulting in an impressive efficiency of 39.90% under LED@2968 K (1062 lux).

Gramine (GRM), which occurs in Gramineae plants, has been developed to be a biological insecticide. Exposure to GRM was reported to induce elevations of serum ALT and AST in rats, but the mechanisms of the observed hepatotoxicity have not been elucidated. The present study aimed to identify reactive metabolites that potentially participate in the toxicity. In rat liver microsomal incubations fortified with glutathione or N-acetylcysteine, one oxidative metabolite (M1), one glutathione conjugate (M2), and one N-acetylcysteine conjugate (M3) were detected after exposure to GRM. The corresponding conjugates were detected in the bile and urine of rats after GRM administration. CYP3A was the main enzyme mediating the metabolic activation of GRM. The detected GSH and NAC conjugates suggest that GRM was metabolized to a quinone imine intermediate. Both GRM and M1 showed significant toxicity to rat primary hepatocytes.

The performance of blue quantum dot light-emitting diodes (QLEDs) is limited by unbalanced charge injection, resulting from insufficient holes caused by low mobility or significant energy barriers. Here, we introduce an angular-shaped heteroarene based on cyclopentane[b]thiopyran (C8–SS) to modify the hole transport layer poly-N-vinylcarbazole (PVK), in blue QLEDs. C8–SS exhibits high hole mobility and conductivity due to the p···p and S···p interactions. Introducing C8–SS to PVK significantly enhanced hole mobility, increasing it by 2 orders of magnitude from 2.44 × 10–6to 1.73 × 10–4cm2V–1s–1. Benefiting from high mobility and conductivity, PVK:C8–SS-based QLEDs exhibit a low turn-on voltage (Von) of 3.2 V. More importantly, the optimized QLEDs achieve a high peak power efficiency (PE) of 7.13 lm/W, which is 2.65 times that of the control QLEDs. The as-proposed interface engineering provides a novel and effective strategy for achieving high-performance blue QLEDs in low-energy consumption lighting applications.

Although X-ray diffraction (XRD) technology has played an essential role in studying the lattice strain of perovskite solar cells (PSCs), accurate construction of the relationship between strains and PSC performance remains challenging due to its limitations. This study investigates the spatial strain distributions of perovskite films on electron transport layers (ETLs) with different surface free energies (γss) through confocal micro-Raman spectroscopy (CMRS) mapping and XRD technology. Results showed that CMRS mapping could more effectively reflect the distribution and size of spatial strain. Uniformed spatial strain with larger grain and preferred orientations can be realized over substrates with optimized γs, corresponding to recombination suppression and interfacial carrier extraction enhancement and significantly reducing open-circuit voltage (VOC) deficits. Optimized PSCs achieve the power conversion efficiency (PCE) of 24.64% and demonstrate excellent compatibility toward large-area or flexible applications, with 20.66% and 22.13% PCEs based on perovskite mini-module and flexible PSCs, respectively.

With the advantages of high precision, strong robustness and dense point clouds (PC), the fringe projection profilometry based binocular structured light technology is widely used in surface defect detection and profile extraction. However, the binocular structured light system (BSLS) often exits the abnormal regions where is coded or encoded ineffectively suffered from the imaging orientation or surface reflectivity of measured object. It will result in poor stereo matching, and the reconstructed data is missing. To improve this phenomenon, a PC fusion method is proposed in this paper. The PC of abnormal regions in the BSLS are fused by the monocular structured light system (MSLS). Two-step method including least squares and iterative closest point is used to get the rigid transformation matrix (RTM) between the BSLS and MSLSs. The strategies of fringe modulation constraint and phase consistency constraint are performed to obtain the reconstruction regions of left and right MSLSs. The feasibility of proposed method is verified both in the theory and experiment analysis. The proposed method improves the integrity of PC in the BSLS without additional equipment, which is attractable.

Person re-identification (Re-ID) has played an extremely crucial role in ensuring social safety and has attracted considerable research attention. 3D shape information is an important clue to understand the posture and shape of pedestrians. However, most existing person Re-ID methods learn pedestrian feature representations from images, ignoring the real 3D human body structure and the spatial relationship between the pedestrians and interferents. To address this problem, our devise a new point cloud Re-ID network (PointReIDNet), designed to obtain 3D shape representations of pedestrians from point clouds of 3D scenes. The model consists of modules, namely global semantic guidance module and local feature extraction module. The global semantic guidance module is designed by enhancing the point cloud feature representation in similar feature neighborhoods and to reduce the interference caused by 3D shape reconstruction or noise. Further, to provide an efficient representation of point clouds, we propose space cover convolution (SC-Conv), which efficiently encodes information on human shapes in local point clouds by constructing anisotropic geometries in the coordinate neighborhoods. Extensive experiments are conducted on four holistic person Re-ID datasets, one occlusion person Re-ID dataset and one point cloud classification dataset. The results exhibit significant improvements over point-cloud-based person Re-ID methods. In particular, the proposed efficient PointReIDNet decreases the number of parameters from 2.30M to 0.35M with an insignificant drop in performance. The source code is available at: https://github.com/changshuowang/PointReIDNet.

Thermal barrier coatings play crucial roles in protecting the hot end components of aero-engines against high-temperature erosion. They must suffer extremely harsh environments including high temperatures, heavy loads, and large internal temperature gradients, which would result in various complex failures. Therefore, it is important to unveil these failure mechanisms to minimize and even prevent them for obvious reasons. Strain/stress evolution between different layers and foreign material erosion are the main failure mechanisms of thermal barrier coatings, which are well suited to be investigated using synchrotron X-ray diffraction. With its tunable energy, high flux, and many other advantages, synchrotron-based X-ray diffraction has become an advanced non-destructive characterization technique for engineering materials yielding important information including their compositions and residual stress which also provides spatial and temporal resolution that is vital for understanding their service performance. This paper presents a concise review of the applications of synchrotron-based X-ray diffraction in investigating thermal barrier coatings to explore their failure mechanisms.

• The hybrid knowledge-assisted data-driven machine learning strategy is utilized to discover A 2 B 2 O 7 -type HEOs with low thermal conductivity through screening 17 rare-earth (RE = Sc, Y, la– Lu) solutes optimized A-site. • The best candidates are addressed and validated among the smart-designed 6188 (5RE 0.2) 2 Zr 2 O 7 HEOs by severe lattice distortion and local phase transformation. • Strong multi-phonon scattering and weak interatomic interactions should be utilized as key property parameters to screen the advanced multi-component HEOs with low thermal conductivity. Lattice engineering and distortion have been considered one kind of effective strategies for discovering advanced materials. The instinct chemical flexibility of high-entropy oxides (HEOs) motivates/accelerates to tailor the target properties through phase transformations and lattice distortion. Here, a hybrid knowledge-assisted data-driven machine learning (ML) strategy is utilized to discover the A 2 B 2 O 7 -type HEOs with low thermal conductivity (κ) through 17 rare-earth (RE = Sc, Y, La–Lu) solutes optimized A-site. A designing routine integrating the ML and high throughput first principles has been proposed to predict the key physical parameter (KPPs) correlated to the targeted κ of advanced HEOs. Among the smart-designed 6188 (5RE 0.2) 2 Zr 2 O 7 HEOs, the best candidates are addressed and validated by the principles of severe lattice distortion and local phase transformation, which effectively reduce κ by the strong multi-phonon scattering and weak interatomic interactions. Particularly, (Sc 0.2 Y 0.2 La 0.2 Ce 0.2 Pr 0.2) 2 Zr 2 O 7 with predicted κ below 1.59 Wm−1 K−1 is selected to be verified, which matches well with the experimental κ = 1.69 Wm−1 K−1 at 300 K and could be further decreased to 0.14 Wm−1 K−1 at 1473 K. Moreover, the coupling effects of lattice vibrations and charges on heat transfer are revealed by the cross-validations of various models, indicating that the weak bonds with low electronegativity and few bonding charge density and the lattice distortion (r *) identified by cation radius ratio (r A / r B) should be the KPPs to decrease κ efficiently. This work supports an intelligent designing strategy with limited atomic and electronic KPPs to accelerate the development of advanced multi-component HEOs with properties/performance at multi-scales. [ABSTRACT FROM AUTHOR]

Perovskite solar cells with the formula FA1−xCsxPbI3, where FA is formamidinium, provide an attractive option for integrating high efficiency, durable stability and compatibility with scaled-up fabrication. Despite the incorporation of Cs cations, which could potentially enable a perfect perovskite lattice1,2, the compositional inhomogeneity caused by A-site cation segregation is likely to be detrimental to the photovoltaic performance of the solar cells3,4. Here we visualized the out-of-plane compositional inhomogeneity along the vertical direction across perovskite films and identified the underlying reasons for the inhomogeneity and its potential impact for devices. We devised a strategy using 1-(phenylsulfonyl)pyrrole to homogenize the distribution of cation composition in perovskite films. The resultant p–i–n devices yielded a certified steady-state photon-to-electron conversion efficiency of 25.2% and durable stability.

Face forgery detection has received considerable attention due to security concerns about abnormal faces generated by face forgery technology. While recent researches have made prominent progress, they still suffer from two limitations: a) the learned features supervised by softmax loss are insufficiently discriminative, since the softmax loss fails to explicitly boost inter-class separability and intra-class compactness; b) hand-crafted features are unable to effectively mine forgery patterns from frequency domain. To address the two problems, this paper proposes a novel frequency-aware discriminative feature learning framework. Specifically, we design an innovative single-center loss which compresses mere intra-class variations of natural faces while encouraging inter-class differences between natural and manipulated faces in the embedding space. Supervised by such a loss, more discriminative features can be learned with less optimization difficulty. As for frequency-related features, a frequency feature adaptively generated module is developed to capture frequency clues in a data-driven manner. Besides, to better fuse the features of both RGB domain and frequency domain, this paper devises a fusion module based on positional correlation of features. The effectiveness and superiority of our framework have been proved by extensive experiments and our approach achieves state-of-the-art performance in both in-dataset and cross-dataset evaluation.

Solution-processable polycrystalline hybrid halide perovskite solar cells have achieved extraordinary efficiencies. However, severe film heterogeneity is prevalent at multiple scales, including composition, lattice structures, and defects, which significantly affects device lifetime. To date, the molecular assembly over lattice-sublattice transformations during film growth is not fully understood. Herein, we reveal the mechanisms of topochemical assembly, wherein a solid-solid transition occurs habitually along the PbI2/perovskite interface. By introducing intermediates, crystal growth follows an alternative pathway along a different coherent interface. As a result, we obtained an optimal (001)-oriented film with minimized lattice heterogeneity, microstructure defects, and electronic disorder. The corresponding inverted device passed the light-induced degradation test certified by the independent third party following the IEC61215 protocols, which retained over 95% of original power conversion efficiency (PCE) after 500 h (AM 1.5G, one sun). Our work unveils the underlying mechanism that governs perovskite crystal synthesis, which is universally obeyed in two-dimensional and inorganic perovskites.

Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.

The utilization factor of blasthole is a crucial indicator of the effectiveness of blasting in rock roadways. A significant value means that the explosive energy is fully utilized, the single-cycle advance is high, and the excavation rate is fast. A good blasting programme is a prerequisite for improving the utilization rate and predicting the utilization rate before blasting operations can verify the feasibility of the blasting programme. Firstly, a database of rock roadway blasting covering different geological and production conditions is established. Secondly, error analysis and the Gini coefficient method are used to weight the characteristic variables, quantify the importance of the variables and identify eight key indicators affecting the blasting hole utilization rate. Then, a random forest algorithm-based model for predicting utilization factor of blasthole is proposed, and the results of the model on the test set are: root mean square error (RMSE) is 0.0137, mean absolute error (MAE) is 0.0087, and coefficient of determination (R2) is 0.905. The performance of this method is compared with that of the neural network and support vector machine models on the test sets to verify the superiority of the random forest algorithm. Finally, to verify the generalization ability and practicality of the random forest prediction model, the model is applied to the rock roadway blasting construction of Gu Bei coal mine in Anhui Province. The results show that R2is 0.913, so the model is reliable and accurate, which can meet the actual engineering requirements and lay the foundation for the promotion and application of this technology.

The solvation capacity of dispersion solvents plays a crucial role in the solution processing of metal halide perovskites. For instance, N,N-dimethylformamide (DMF), a widely used dispersion solvent, possesses high solvation capacity but often generates suboptimal film quality due to slow crystallization kinetics. We propose using low-solvation binary cosolvents (nitrile- and ether-type solvents) to achieve a balance between solvation (i.e., sufficient solubility of precursors) and desolvation (i.e., rapid crystallization of films) processes during perovskite synthesis. The polarity and hydrogen-bonding property of these cosolvents synergistically enhance their solvation capacity, facilitating perovskite precursor dissolution. Moreover, the low-solvation cosolvents accelerate the crystallization of well-defined intermediate films, yielding higher-quality perovskites than those synthesized with DMF. The optimized modules achieved an active-area efficiency of 22.27%, with a certified aperture-area efficiency of 16.10% and corresponding active-area efficiency of 20.75%. This research on solvation regulation provides universal guidelines for innovatively preparing high-quality halide perovskites.

Ionic liquid (IL) additives proved to have a positive effect on the device efficiency and stability of perovskite solar cells. However, since ILs are small molecules and undergo Coulomb interactions, they can easily aggregate and evaporate over long times, which would cause instabilities during a long-term device operation. To overcome these problems, we polymerize ILs into macromolecules and incorporate them into perovskite films as well as into the corresponding solar cells. Both cations and anions of the used poly[1-(2-acryloylethyl)-3-methylimidazolium] bis (trifluoromethane) sulfonamides (PAEMI-TFSIs) are designed to coordinate with the Pb and I of PbI62–octahedra, respectively, which changes the crystallization behavior of the perovskite films. Importantly, the PAEMI-TFSI efficiently passivates electronic defects on the grain boundaries and thereby enhances the charge-carrier transport in the perovskite film. As a result, PAEMI-TFSI-modified MAPbI3solar cells show a high power conversion efficiency of 22.4% and an excellent storage stability (92% of the initial efficiency remains after 1200 h operation in a nitrogen atmosphere for nonencapsulated devices).

The phase transformation from the TiS2to TiS on the surface of bulk 1T-TiS2crystals is achieved by thermal vacuum annealing. This is demonstrated by a combination of photoemission spectroscopy, electron paramagnetic resonance spectroscopy, and Raman spectroscopy. The chemical interaction between TiS2and surface-adsorbed H2O molecules is expected to assist this transformation. Especially, the transformation temperature is found to depend on the natural defects present in the TiS2crystals. Ti interstitial (Tii) and S vacancy (Sv) defects hinder TiS2to TiS transformation, whereas Ti-OH species facilitate the phase transformation. These results not only provide a valuable insight into the water-assisted phase transformation modulated by defects but also highlight a potential strategy for designing an unusual TiS2-TiS heterostructure through a combination of phase and defect engineering.

Modifying the exposed upper surface of perovskite solar cells (PSCs) has greatly contributed to improving their photovoltaic performance. The equally important buried interface (that is, the hidden bottom of perovskite film and the beginning of perovskite film crystallization) is much less studied due to great difficulties in tailoring it. Here we expose the large-area buried interface non-destructively for direct investigation. We find that the disordered beginning of the perovskite film growth deteriorates the buried interface. To address this issue, instead of using a passivator, we synthesize a transparent and conductive oxide perovskite (SrSnO3) to act as the electron transport layer. The high lattice matching enables a more ordered beginning of the growth of halide perovskite on the electron transport layer, avoiding the formation of a deteriorated buried interface. The constructed buried interface exhibits suppressed defects, strain, better crystallinity, reduced ion migration and fewer voids. The best performing PSCs deliver a power conversion efficiency of 25.17%. Moreover, PSCs with an initial power conversion efficiency of 24.4% maintain 90% of the original value after operating for 1,000 h.

Deep neural networks have been successfully investigated in the computational analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, such as Alzheimer's disease (AD). The disease-related changes in sMRI may be different in local brain regions, which have variant structures but with some correlations. In addition, aging increases the risk of dementia. However, it is still challenging to capture the local variations and long-range correlations of different brain regions and make use of the age information for disease diagnosis. To address these problems, we propose a hybrid network with multi-scale attention convolution and aging transformer for AD diagnosis. First, to capture the local variations, a multi-scale attention convolution is proposed to learn the feature maps with multi-scale kernels, which are adaptively aggregated by an attention module. Then, to model the long-range correlations of brain regions, a pyramid non-local block is used on the high-level features to learn more powerful features. Finally, we propose an aging transformer subnetwork to embed the age information into image features and capture the dependencies between subjects at different ages. The proposed method can learn not only the subject-specific rich features but also the inter-subject age correlations in an end-to-end framework. Our method is evaluated with T1-weighted sMRI scans from a large cohort of subjects on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that our method has achieved promising performance for AD-related diagnosis.

Intelligent manufacturing technology has recently experienced rapid development, where robot welding systems are an essential component. A laser profile sensor is commonly utilized in intelligent robot welding systems. However, when the laser stripe is projected onto a narrow weld joint, the deformation of the laser stripe is not sufficiently apparent, and the traditional weld joint recognition methods based on the laser profile sensors cannot detect the narrow weld joint position. Meanwhile, the strong arc and spatter noise during welding and the expansion of weld joint width caused by welding thermal expansion are likely to interfere with weld joint recognition. In addition, many common weld seam recognition methods can only recognize certain weld joints and have limited flexibility and robustness for other types of weld joints. This study proposes a wide range of weld joint recognition algorithms based on the laser profile sensor to overcome these challenges. The gray value histogram analysis method, the adaptive threshold method, the internal propulsion center and boundary extraction (IPCBE) method, the coordinate analysis method, and the stripe end center detection method proposed in this study are used primarily in the algorithm. The algorithm solves the problem of identifying narrow weld joints and allows for the identification of larger conventional weld joints. Furthermore, the algorithm has good a anti-spatter and anti-arc interference ability and a fast speed, allowing it to meet the demand for real-time welding identification.

Unsupervised 2D image-based 3D shape retrieval aims to match the similar 3D unlabeled shapes when given a 2D labeled sample. Although a lot of methods have made a certain degree of progress, the performance of this task is still restricted due to the lack of target labels resulting in tremendous domain gap. In this paper, we aim to explore the discriminative representation of the unlabeled target 3D shapes and facilitate the procedure of domain adaptation by taking full advantage of multi-view information. To achieve the above goals, we propose an effective self-supervised auxiliary domain alignment (SADA) for unsupervised 2D image-based 3D shape retrieval. SADA mainly contains multi-view guided self-supervised feature learning and two auxiliary domain alignments, including intermediate domain alignment and multi-domain alignment. Firstly, we group multiple views of each 3D shape into two sub-target domains based on the view similarities and regard each other as the constraint to optimize the feature learning in an unsupervised manner. To reduce the difficulty of directly aligning the domain discrepancy, we combine the source labeled samples and target samples (pseudo labels) with the same category to generate an intermediate domain, which translates the source-target alignment into source-intermediate and intermediate-target alignments. Moreover, to explore the inner characteristics of target 3D shapes and provide more clues for better adaptation, multi-domain alignment is proposed to convert the source and single target domain alignment to the source and multiple target domain (one target domain and two sub-target domains) alignments. The adversarial training and semantic alignment are employed to fully excavate the relations between source domain and multiple target domains. Experiments on two challenging datasets show that the proposed method achieves competing performance in the unsupervised 2D image-based 3D shape retrieval task.

Direct and stable α-phase formation of formamidinium lead triiodide (FAPbI3) perovskite serves as an indispensable prerequisite for constructing stable and efficient perovskite solar cells. However, the α-phase formation is highly limited by molecular additives. Here, we report a strategy for directly fabricating stable α-phase of FAPbI3perovskite by controlling the precursor solution on the molecular level using methylammonium acetate (MAAc) ionic liquid. We found that a strong interaction between Ac−and Pb2+via C=O···Pb chelation enables the relaxation of Pb–I bonds and thus allows the effective insertion of formamidinium iodide (FAI) into soft Pb–I framework. Molecular dynamics simulations also show that the precursor solution has undergone an exchange of coordination between I−and Ac−to provide the reaction sites for FAI. With a one-step, antisolvent-treatment-free, and air-processing approach regardless of humidity, the corresponding photovoltaic cell exhibits an efficiency close to 24% along with long operational stability over 1,000 h.

Electron spins in van der Waals materials are playing a crucial role in recent advances in condensed-matter physics and spintronics. However, nuclear spins in van der Waals materials remain an unexplored quantum resource. Here we report optical polarization and coherent control of nuclear spins in a van der Waals material at room temperature. We use negatively charged boron vacancy (VB−) spin defects in hexagonal boron nitride to polarize nearby nitrogen nuclear spins. We observe the Rabi frequency of nuclear spins at the excited-state level anti-crossing of VB−defects to be 350 times larger than that of an isolated nucleus, and demonstrate fast coherent control of nuclear spins. Further, we detect strong electron-mediated nuclear–nuclear spin coupling that is five orders of magnitude larger than the direct nuclear-spin dipolar coupling, enabling multi-qubit operations. Our work opens new avenues for the manipulation of nuclear spins in van der Waals materials for quantum information science and technology.

As a green solvent, ionic liquids (ILs) are considered as a promising alternative to conventional polar aprotic solvents for the production of efficient and stable perovskite solar cells (PSCs). Moreover, with the use of IL solvents, perovskite films can be prepared without antisolvent treatments in an ambient environment instead of in a glovebox with inert gases, which simplifies the film manufacturing process and is favorable for industrialization production. However, the type of IL solvents that have been studied is limited, and the influence of IL molecular structures on the perovskite-film crystallization and device performance is not completely understood. In this work, four different ILs, methylammonium formate (MAF), methylammonium acetate (MAAc), methylammonium propionate (MAP), and mthylammonium isobutyrate (MAIB), are synthesized as the perovskite precursor solvents. The interaction between the functional groups of the synthesized solvents and Pb2+in the precursor solution is studied, which has a direct impact on the morphology and crystallization of the deposited perovskite film. It is found that MAP solvent gives a high-quality perovskite film, which leads to the best photovoltaic performance with a champion PCE of 20.56% compared to the devices based on the other IL solvents. Moreover, the MAP-based device maintains 88% of its original PCE after 1000 h of storage in a N2atmosphere, demonstrating excellent device stability. Therefore, it is concluded that MAP is the most suitable solvent for MAPbI3films with respect to photovoltaic applications as compared to the other ILs.

Efficient Sn-based perovskite solar cell device with power conversion efficient of 10.73% and low hysteresis effect is reported, where chiral cation is adopted to promote the formation of 2D/3D structure of the perovskite film and facilitate the selective hole transportation at the interface of the device.

High-quality large-area perovskite films are prepared using a solid–liquid two-step film formation method combined with CsBr modification for the buried interface and Urea additive for perovskite crystallization.The inverted perovskite solar modules’ performance is enhanced to 20.56% in 61.56 cm2with improved stability.

Enhancing the quality of junctions is crucial for optimizing carrier extraction and suppressing recombination in semiconductor devices. In recent years, metal halide perovskite has emerged as the most promising next-generation material for optoelectronic devices. However, the construction of high-quality perovskite junctions, as well as characterization and understanding of their carrier polarity and density, remains a challenge. In this study, using combined electrical and spectroscopic characterization techniques, we investigate the doping characteristics of perovskite films by remote molecules, which is corroborated by our theoretical simulations indicating Schottky defects consisting of double ions as effective charge dopants. Through a post-treatment process involving a combination of biammonium and monoammonium molecules, we create a surface layer of n-type low-dimensional perovskite. This surface layer forms a heterojunction with the underlying 3D perovskite film, resulting in a favorable doping profile that enhances carrier extraction. The fabricated device exhibits an outstanding open-circuit voltage (VOC) up to 1.34 V and achieves a certified efficiency of 19.31% for single-junction wide-bandgap (1.77 eV) perovskite solar cells, together with significantly enhanced operational stability, thanks to the improved separation of carriers. Furthermore, we demonstrate the potential of this wide-bandgap device by achieving a certified efficiency of 27.04% and a VOCof 2.12 V in a perovskite/perovskite tandem solar cell configuration.Effective electrical doping of perovskite caused by remote molecular dopants are precisely characterized and clarified, based on which a 3D-2D heterojunction structure is fabricated to achieve a high efficiency wide bandgap perovskite solar cell, and an all perovskite tandem solar cell with efficiency over 27%.

Fluorinated spacer cations in quasi-2D (Q-2D) perovskites have recently been demonstrated to improve the Q-2D perovskite solar cell (PSC) performance. However, the underlying mechanism of fluorination of organic cations on the improvement is still unclear. Here, using fluorinated benzylammonium (named F-BZA) as a spacer cation in Q-2D Ruddlesden–Popper (RP) perovskites, we deeply investigate the effect of fluorination of organic cations on perovskite crystallization and intermolecular interactions for improving the charge transport and device performance. It is found that fluorination of spacer cations can slow down the crystallization rate of perovskites, resulting in vertically aligned large grains. Moreover, the interaction between the adjacent spacer cations is further enhanced, constructing a new faster charge-transport channel with a lifetime of 77 ps. Accordingly, the carrier mobility is improved by an order of magnitude and a power conversion efficiency (PCE) of 16.82% is achieved in much more stable F-BZA-based Q-2D RP PSCs, 35% higher than that of BZA-based devices (12.39%). Our results elucidate the mechanism and its importance of fluorinating spacer cations for high-performance Q-2D PSC development.

One of the fundamental predictions of quantum mechanics is the occurrence of random fluctuations in a vacuum caused by the zero-point energy. Remarkably, quantum electromagnetic fluctuations can induce a measurable force between neutral objects, known as the Casimir effect1, and it has been studied both theoretically2,3and experimentally4–9. The Casimir effect can dominate the interaction between microstructures at small separations and is essential for micro- and nanotechnologies10,11. It has been utilized to realize nonlinear oscillation12, quantum trapping13, phonon transfer14,15and dissipation dilution16. However, a non-reciprocal device based on quantum vacuum fluctuations remains an unexplored frontier. Here we report quantum-vacuum-mediated non-reciprocal energy transfer between two micromechanical oscillators. We parametrically modulate the Casimir interaction to realize a strong coupling between the two oscillators with different resonant frequencies. We engineer the system’s spectrum such that it possesses an exceptional point17–20in the parameter space and explore the asymmetric topological structure in its vicinity. By dynamically changing the parameters near the exceptional point and utilizing the non-adiabaticity of the process, we achieve non-reciprocal energy transfer between the two oscillators with high contrast. Our work demonstrates a scheme that employs quantum vacuum fluctuations to regulate energy transfer at the nanoscale and may enable functional Casimir devices in the future.

Adding 2D perovskite to 3D perovskite has been developed to effectively enhance the intrinsic stability, but it will inevitably compromise the power conversion efficiency (PCE). Here, in a novel way, we introduced highly oriented 2D (BDA)PbI4perovskites as seeds to optimize the growth kinetics of 3D perovskite and make its crystallization directly stride over the nucleation stage. Therefore, the seeds preferentially act as templates to epitaxially grow 3D perovskite with the desired facet orientation and stacking mode. Moreover, the 2D seeds are transformed into the grain boundary after completing the orientation-induced growth. As a result, the high-quality mixed-dimensional perovskite film delivers a superior PCE of 23.95%, accompanied by a remarkable FF of 0.847. The unencapsulated Perovskite solar cell (PSC) retains 93% of its original efficiency after 1,056 h of storage and retains 91% of its initial efficiency after 500 h of operation at the maximum power point under simulated AM1.5 illumination at 60°C.

Natural background levels (NBLs) and threshold values (TVs) are two important parameters for identifying and quantifying groundwater pollution. Given this, this study investigated the spatiotemporal changes and affecting factors of NBLs and TVs for the phreatic groundwater in different environmental units divided as per the geomorphological units, the geological structures, the catchment conditions, and the types of the aquifer media in the Greater Xi'an area of China. The groundwater environmental background values in each unit were determined using the Grubbs method. The NBLs and TVs of K+, Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, and TDS were then estimated and discussed with respect to their changes associated natural and anthropogenic processes. The results show that all ions have exceeded their standard limits to different degrees. The water quality in 2020 was worse than that in 2015. The NBLs of the groundwater hydrochemical components in the northern Wei River zone were higher compared with those in the southern Wei River zone during the study period due to the differences in geological formations, rainfall, evaporation and ground surface slope. In agricultural areas, agricultural activities may lead to the percolation of surface pollutants into the deeper parts of the aquifer with the irrigation water, causing the NBLs in the alluvial plain area being generally higher. In addition, urbanization also affects the hydrochemical compositions of groundwater. The high NBLs of TDS, Mg2+, SO42−, HCO3−in the area near Weinan City may be due to the over-exploitation of groundwater. The spatiotemporal evaluation of NBLs lays a foundation for determining the scope and sources of groundwater pollution, and may support the effective groundwater development, utilization, and protection.