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The oxide and halide perovskite materials with a ABX3 structure exhibit a number of excellent properties, including a high dielectric constant, electrochemical properties, a wide band gap, and a large absorption coefficient. These properties have led to a range of applications, including renewable energy and optoelectronics, where high-performance catalysts are needed. However, it is difficult for a single structure of perovskite alone to simultaneously fulfill the diverse needs of multiple applications, such as high performance and good stability at the same time. Consequently, perovskite nanocomposites have been developed to address the current limitations and enhance their functionality by combining perovskite with two or more materials to create complementary materials. This review paper categorizes perovskite nanocomposites according to their structural composition and outlines their synthesis methodologies, as well as their applications in various fields. These include fuel cells, electrochemical water splitting, CO2 mitigation, supercapacitors, and optoelectronic devices. Additionally, the review presents a summary of their research status, practical challenges, and future prospects in the fields of renewable energy and electronics. [ABSTRACT FROM AUTHOR]

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input's contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters. [ABSTRACT FROM AUTHOR]

Herein, multiwalled carbon nanotube (MWCNT)/silicon nitride (Si3N4)/polyaniline (PANI) ternary composites are prepared. The preparation method involves attaching carboxyl groups to acid‐modified MWCNT surfaces and amino groups to Si3N4 surfaces modified using a silane‐coupling agent. Then they are combined to binary composites using the solvent‐thermal method. Finally, the ternary composites are prepared by coating PANI on the surface of MWCNT/Si3N4 composites in situ polymerization. The morphology and structure of these composites are characterized through X‐ray diffraction, Fourier‐transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, and X‐ray photoelectron spectroscopy. Results show that the reflection loss at 8.8 GHz reaches −42.57 dB when the feeding ratio of MWCNT:Si3N4:PANI is 3:20:40 and the corresponding effective absorption bandwidth reaches 4.06 GHz when the thickness is 3.5 mm. A conductive network with effective electron leaps is formed among MWCNT, Si3N4, and PANI, which increases the conductive loss of the composites. An abundant number of interfaces have formed in the composite materials and promoted dielectric loss. Multiple loss mechanisms and good impedance matching performance endow MWCNT/Si3N4/PANI with excellent microwave absorption performance, and the incorporation of Si3N4 enables the composites to exhibit satisfactory high‐temperature resistance. Thus, these performances render the composites promising materials to address increasing electromagnetic pollution, particularly at high temperatures. [ABSTRACT FROM AUTHOR]

Optical computing promises to improve the speed and energy efficiency of machine learning applications1–6. However, current approaches to efficiently train these models are limited by in silico emulation on digital computers. Here we develop a method called fully forward mode (FFM) learning, which implements the compute-intensive training process on the physical system. The majority of the machine learning operations are thus efficiently conducted in parallel on site, alleviating numerical modelling constraints. In free-space and integrated photonics, we experimentally demonstrate optical systems with state-of-the-art performances for a given network size. FFM learning shows training the deepest optical neural networks with millions of parameters achieves accuracy equivalent to the ideal model. It supports all-optical focusing through scattering media with a resolution of the diffraction limit; it can also image in parallel the objects hidden outside the direct line of sight at over a kilohertz frame rate and can conduct all-optical processing with light intensity as weak as subphoton per pixel (5.40 × 1018- operations-per-second-per-watt energy efficiency) at room temperature. Furthermore, we prove that FFM learning can automatically search non-Hermitian exceptional points without an analytical model. FFM learning not only facilitates orders-of-magnitude-faster learning processes, but can also advance applied and theoretical fields such as deep neural networks, ultrasensitive perception and topological photonics.We present fully forward mode learning, which conducts machine learning operations on site, leading to faster learning and promoting advancement in numerous fields. [ABSTRACT FROM AUTHOR]

In this paper, a long-stroke parallel compliant tip-tilt-piston micropositioning stage driven by voice coil motors (VCMs) is proposed. The stage is equipped with three sets of driving arms, which include a spherical hinge, VCM, and parallelogram guide mechanism, evenly spaced at 120° intervals. The spherical hinge is composed of orthogonal leaf-spring beams, and the VCM is embedded into the parallelogram mechanism to form a compact design. The compliance matrix method and the geometric method facilitated the determination of compliance in all six degree-of-freedom directions of the spherical hinge and the derivation of kinematic equations for decoupling the motion of the stage. In addition, finite element analysis was utilized to determine the maximum stroke and stress of the stage. To validate the proposed design, a stage prototype was constructed and subjected to experimental testing. Furthermore, a feedback controller was designed, integrating proportional integral controller, notch filter, and sliding mode controller feedforward. The experimental results indicate that the stage can achieve a long stroke of ±50.75 mrad × ±44.2 mrad × ±4.425 mm, with the natural frequencies in the three-axis direction of 22.3 × 25.5 × 25.5 Hz3. In addition, the maximum relative tracking error was maintained below 5.25%, highlighting the effectiveness of the control technique in achieving a high tracking performance. [ABSTRACT FROM AUTHOR]

Wildfires cause great losses to the ecological environment, economy, and people's safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. [ABSTRACT FROM AUTHOR]

Menstrual blood-derived endometrial stem cells (MenSCs) have attracted increasing interest due to their excellent safety, and lack of ethical dilemma as well as their ability to be periodically obtained in a noninvasive manner. However, although preclinical research as shown the therapeutic potential of MenSCs in several diseases, their poor cell survival and low engraftment at disease sites reduce their clinical efficacy. Flotillins (including Flot1 and Flot2) are implicated in various cellular processes, such as vesicular trafficking, signal transduction, cell proliferation, migration and apoptosis. In this study, we aimed to determine the effects of Flotillins on MenSCs survival, proliferation and migration. Our experimental results show that MenSCs were modified to overexpress Flot1 and/or Flot2 without altering their intrinsic characteristics. Flot1 and Flot2 co-overexpression promoted MenSC viability and proliferation capacity. Moreover, Flot1 or Flot2 overexpression significantly promoted the migration and inhibited the apoptosis of MenSCs compared with the negative control group, and these effects were stronger in the Flot1 and Flot2 gene co-overexpression group. However, these effects were significantly reversed after Flot1 and/or Flot2 knockdown. In conclusion, our results indicate that Flot1 and Flot2 overexpression in MenSCs improved their proliferation and migration and inhibited their apoptosis, and this might be an effective approach to improve the efficiency of cell-based therapies. [ABSTRACT FROM AUTHOR]

Fe3O4/SiO2 nanocomposites (NCs) were prepared by the interaction of alkyne-functionalized ferric oxide (Fe3O4-Alk) nanoparticles (NPs) and azide-functionalized silicon dioxide (SiO2-N3) NPs via the click reaction. The morphology and structure of the Fe3O4/SiO2 NCs were investigated using scanning electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and infrared analyses, and the electromagnetic parameters were measured using a vector network analyzer. For the Fe3O4/SiO2 nanocomposites obtained via the click reaction, the minimum reflection loss reached − 37.15 dB at 16.68 GHz and a matching thickness of 5.0 mm, and the bandwidth below − 10 dB ranged from 3.2 to 6.7 GHz and 15.7 to 17.6 GHz. The Fe3O4/SiO2 composites synthesized via the click reaction exhibited better microwave absorption performance than the physically blended sample. This is mainly because Fe3O4 and SiO2 are covalently bonded through the click reaction in the composites, creating multiple heterogeneous interfaces, which enhance dielectric loss and impedance matching. [ABSTRACT FROM AUTHOR]

Profit distribution in sequential innovation is a crucial yet relatively unexplored area of empirical research. With novel cross-section data from the game live-streaming industry, we are the first to assess the contribution shared by initial and follow-on innovators empirically. Unlike the complex innovation paths of patents, the copyright structure within the game live-streaming industry is clear and concise, enabling direct measurement of innovation value. At the industry's average level, the share stands at 1:2 between game producers and streamers. This measurement remains robust even after controlling for income levels and distinguishing between professionals and amateurs. Nevertheless, significant heterogeneity exists across different game genres. We suggest that a balanced distribution scheme between initial and follow-on innovators should seriously consider their contribution shares. [ABSTRACT FROM AUTHOR]

This paper addresses the stochastic linear quadratic (LQ) control problem with first‐ and second‐order moment constraints on the terminal state. The problem is a modified version of the optimal covariance control problem, where the terminal state is steered to a given probability distribution. Studying a multiplicative‐noise stochastic system rather than an additive‐noise system is a salient feature. Unlike the existing ideas in the optimal steering, by using the Lagrange multipliers method and establishing the stochastic maximum principle, our problem is converted into solving forward–backward stochastic difference equations (FBSDEs), which is a special stochastic two‐point boundary‐value problem (TPBVP). We provide the optimal closed‐loop controller and necessary and sufficient solvability conditions by developing a nonhomogeneous relationship between the optimal state and costate in FBSDEs. Finally, numerical examples are given to demonstrate our results. [ABSTRACT FROM AUTHOR]

NLRP inflammasomes are a group of cytosolic multiprotein oligomer pattern recognition receptors (PRRs) involved in the recognition of pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) produced by infected cells. They regulate innate immunity by triggering a protective inflammatory response. However, despite their protective role, aberrant NLPR inflammasome activation and gain-of-function mutations in NLRP sensor proteins are involved in occurrence and enhancement of non-communicating autoimmune, auto-inflammatory, and neurodegenerative diseases. In the last few years, significant advances have been achieved in the understanding of the NLRP inflammasome physiological functions and their molecular mechanisms of activation, as well as therapeutics that target NLRP inflammasome activity in inflammatory diseases. Here, we provide the latest research progress on NLRP inflammasomes, including NLRP1, CARD8, NLRP3, NLRP6, NLRP7, NLRP2, NLRP9, NLRP10, and NLRP12 regarding their structural and assembling features, signaling transduction and molecular activation mechanisms. Importantly, we highlight the mechanisms associated with NLRP inflammasome dysregulation involved in numerous human auto-inflammatory, autoimmune, and neurodegenerative diseases. Overall, we summarize the latest discoveries in NLRP biology, their forming inflammasomes, and their role in health and diseases, and provide therapeutic strategies and perspectives for future studies about NLRP inflammasomes. [ABSTRACT FROM AUTHOR]

Sediment deposition significantly impacts soil erosion processes, consequently influencing the geographical morphology and surrounding environments of reservoirs and estuaries. Given the intricate nature of sediment deposition, it is imperative to consolidate and analyze existing research findings. Presently, studies on sediment settling velocity primarily employ theoretical, laboratory, and field experimentation methods. Theoretical approaches, rooted in mechanics, examine the various forces acting on sediment particles in water to derive settling velocity equations. However, they often overlook external factors like temperature, salinity, organic matter, and pH. Although laboratory experiments scrutinize the influence of these external factors on sedimentation velocity, sediment settling is not solely influenced by individual factors but rather by their collective interplay. Field observations offer the most accurate depiction of sediment deposition rates. However, the equipment used in such experiments may disrupt the natural sedimentation process and damage flocs. Moreover, measurements of sediment particle size from different instruments yield varied results. Additionally, this paper synthesizes the impact of suspended sediment concentration, particle size, shape, temperature, salinity, and organic matter on sediment settling velocity. Future research should focus on innovating new laboratory observation methods for sediment settling velocity and utilizing advanced scientific and technological tools for on-site measurements to provide valuable insights for further investigation into sediment settling velocity. [ABSTRACT FROM AUTHOR]

Developing highly active and stable nanocarbon electrocatalysts for the oxygen reduction reaction (ORR) in alkaline medium has attracted much attention due to their potential as an alternative to traditional metal‐based or noble metal catalysts. Herein, a unique micro‐nano structure of (B, N)‐rich B−C−N nanosheet‐assembled microwires (BCNNAM) were synthesized and driven electrochemical oxide reduction reaction. The diameter of the microwires about 1 μm, while the nanosheets have an average thickness of less than 20 nm. The compact nanosheets are mostly separated with a bending, curling and crumpling morphology. All those constitutes a ternary system with large surface area and abundant activity sites facilitate fast mass/electron transport for ORR catalytic activity. Thus, the B, N‐rich B−C−N nanosheet‐assembled microwires show significantly improved electrocatalytic activity with an onset potential of 0.95 V and half‐wave potential of 0.83 V compared to BNNAM, nitride‐doped carbon, porous carbon and a commercial Pt/C electrocatalyst. Such high catalytic performance of B, N‐rich B−C−N micro‐nano structures is due to the enhanced activity by the coexistence of B, C and N and the mass transfer promoted by the unique hierarchical porous structure. [ABSTRACT FROM AUTHOR]

N6‐methyladenosine (m6A) is one of the main epitranscriptomic modifications that accelerates the progression of malignant tumors by modifying RNA. Methyltransferase‐like 16 (METTL16) is a newly identified methyltransferase that has been found to play an important oncogenic role in a few malignancies; however, its function in osteosarcoma (OS) remains unclear. In this study, METTL16 was found to be upregulated in OS tissues, and associated with poor prognosis in OS patients. Functionally, METTL16 substantially promoted OS cell proliferation, migration, and invasion in vitro and OS growth in vivo. Mechanistically, vacuolar protein sorting protein 33b (VPS33B) was identified as the downstream target of METTL16, which induced m6A modification of VPS33B and impaired the stability of the VPS33B transcript, thereby degrading VPS33B. In addition, VPS33B was found to be downregulated in OS tissues, VPS33B knockdown markedly attenuated shMETTL16‐mediated inhibition on OS progression. Finally, METTL16/VPS33B might facilitate OS progression through PI3K/AKT pathway. In summary, this study revealed an important role for the METTL16‐mediated m6A modification in OS progression, implying it as a promising target for OS treatment. [ABSTRACT FROM AUTHOR]

Employing volatile solid additives have emerged as a promising method to optimize the morphology and improve the performance of organic solar cells (OSCs). However, principles governing the efficient design of solid additives remain elusive. Herein, the programmed fluorination and/or bromination on benzene core to develop efficient additives for OSCs is reported. The programmed fluorination and/or bromination endow the five halogen benzene derivatives, 1,3,5‐trifluorobenzene, hexafluorobenzene, 1,3,5‐tribromo‐2,4,6‐trifluorobenzene (TFTB), 1,3,5‐tribromobenzene, and hexabromobenzene, with different melting and boiling points, volatility, as well as interactions with the host blend. Studies indicate that the additives with extremely high and low volatility are almost powerless and even detrimental to the morphology evolution. Among them, the combination of fluorine and bromine atoms on TFTB not only enables the more appropriate m.p./b.p. and volatility, but also exerts stronger molecular interactions with the host blend, giving rise to higher ordered molecular packing and more favorable morphology. Importantly, TFTB exhibits good universality to optimize the performances of OSCs with high power conversion efficiencies (PCEs; over 18%) in a group of binary blend systems, and an impressive PCE of 19.43% in the ternary PBTz‐F:PM6:L8‐BO system. [ABSTRACT FROM AUTHOR]

With the development of advanced micro/nanoscale technologies, two-dimensional materials have emerged from laboratories and have been applied in practice. To investigate the mechanisms of solid–liquid interactions in potential applications, molecular dynamics simulations are employed to study the flow behavior of n-dodecane (C12) molecules confined in black phosphorus (BP) nanochannels. Under the same external conditions, a significant difference in the velocity profiles of fluid molecules is observed when flowing along the armchair and zigzag directions of the BP walls. The average velocity of C12 molecules flowing along the zigzag direction is 9-fold higher than that along the armchair direction. The friction factor at the interface between C12 molecules and BP nanochannels and the orientations of C12 molecules near the BP walls are analyzed to explain the differences in velocity profiles under various flow directions, external driving forces, and nanochannel widths. The result shows that most C12 molecules are oriented parallel to the flow direction along the zigzag direction, leading to a relatively smaller friction factor hence a higher average velocity. In contrast, along the armchair direction, most C12 molecules are oriented perpendicular to the flow direction, leading to a relatively larger friction factor and thus a lower average velocity. This work provides important insights into understanding the anisotropic liquid flows in nanochannels. [ABSTRACT FROM AUTHOR]

In this paper, we study the existence of traces for Sobolev spaces on the hyperbolic filling X of a compact metric space Z equipped with a doubling measure. Given a suitable metric on X, we can regard Z as the boundary of X. After equipping X with a weighted measure μ ρ via the measure on Z and the Euclidean arc length, we give characterizations for the existence of traces for first-order Sobolev spaces. [ABSTRACT FROM AUTHOR]

In this paper, by means of forming alternately broad-narrow band gap unit in the backbones of polymers to regulate polymer light-emitting color, a series of ethynyl-linked alternating aromatic ring oligomers (P1, P2, P3 and P4) were designed and prepared by Sonogashira coupling polycondensation. These polymers have Mw 35524–12852 in moderate yields (35–67%). The influence of this kind of polymer's structures on the optical properties was studied in detail with 1H NMR,13C NMR, FTIR, UV, FL, CV, TGA, and fluorescence lifetime test. The results showed that these polymers exhibited good solubility, high thermal stabilities, high oxidative stability, stable optical properties and emitted variously colored fluorescence in moderate quantum yields (28–61%) due to their different energy gap. The fluorescence lifetimes of the polymers P1, P2, P3, and P4 were 0.8974, 1.695, 1.728, and 0.5929 ns, respectively. And the values of lifetime decrease with increasing solvent polarity. The effective light-emitting color regulation of poly(aryleneethynylene)s was realized by Gaussian calculation and experiment results. [ABSTRACT FROM AUTHOR]

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN-LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%-72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather. [ABSTRACT FROM AUTHOR]

Sediment transport serves as a link for material exchange between land and sea. Using sediment traps, we can observe the capture and transport processes of sediments. Based on the sediment particle size distribution characteristics in Jiaozhou Bay, this paper analyzes the influence of a newly designed 3D sediment trap on the water–sand two-phase flow process inside and outside a trap device during its operation. Meanwhile, under a certain concentration condition, a numerical formula model is researched and proposed to evaluate the impact of the device's structure, the environmental flow speed, and the particle size on particle capture efficiency. This model is based on the CFD-DPM coupling in Fluent 2021R1 software, and the particle filtration process is solved using a combination of porous media and UDF functions. Finally, by analyzing the distribution of sediment movement in the fluid domain, two concepts, namely the percentage of particles entering the tube and the effective capture rate, are proposed. Suggestions for optimizing the structure of the trap are put forward to achieve optimal capture effects. [ABSTRACT FROM AUTHOR]

A monolithic perovskite/silicon tandem solar cell architecture is employed to surpass the single‐junction efficiency limit. Recently, there is an increasing need for the double‐sided textures in the Si bottom cell to be compatible with the solution‐processed perovskite top cell from an industrial perspective. Herein, a silver‐assisted alkaline etching method is applied to fabricate nanoscale Si pyramid textures, and the influence of varying pyramid size (400–900 nm) on the interface morphology and the performance of perovskite/Si tandem cells is investigated. It is demonstrated that electrical shunting starts to increase, and the open‐circuit voltage (VOC) decreases when the texture size exceeds the perovskite thickness (~500nm) due to the non‐uniform top‐cell formation on a rough Si surface. However, when the texture size is reduced to 400–500 nm, all spin‐coated perovskite top‐cell component layers exhibit an even form over the nanopyramid Si, resulting in a high VOC and an enhanced Si bottom cell current (≈1.0 mA cm−2) due to the suppressed reflectance at the top/bottom cell interface without using optical couplers. The double‐sided nanopyramid Si texture offers opportunities to increase tandem cell efficiency while reducing its production cost compared with the commonly used single‐sided textured Si. [ABSTRACT FROM AUTHOR]

Reservoir operation policies affect sediment methane (CH4) production and pathways through complex influence on hydrodynamic and biogeochemical processes in reservoir water and sediment. However, the underlying influencing mechanisms, especially the impact of operations on sediment CH4 production and pathways, remain poorly understood. This study combines a physical‐biogeochemical model with a reservoir operation model to evaluate operation impacts on spatiotemporal sediment CH4 production and release dynamics and to analyze affiliated processes and driving factors. Three typical operation policies (i.e., standard, hedging, eco‐friendly) are selected to create the operation scenarios, and a coupled physical‐biogeochemical model is adopted to simulate and compare sediment CH4 production, consumption, diffusion, and ebullition under these scenarios. The methods are applied in the second largest reservoir in China (i.e., Danjiangkou Reservoir) as a case study. Results show that annual total sediment CH4 production among the scenarios ranges from 285.9 to 298.3 Gg C year−1, while operation scenario outcomes significantly differ. Specifically, a significant CH4 percentage (34%–39%) is oxidized in surface sediment while the remaining escapes sediment through diffusion and ebullition. Although diffusion accounts for >60% of the annual sediment CH4 release, sediment ebullition ultimately dominates atmospheric CH4 emissions. Annual total atmospheric CH4 emissions range from 42.8 to 71.1 Gg C year−1 and account for 14%–25% of the annual total sediment production among the three scenarios. The differences demonstrate that reservoir operations significantly impact CH4 pathways and atmospheric emissions. This study provides implications for reservoir managers to coordinate socioeconomic and ecological protection while mitigating CH4 emission targets. Key Points: CH4 dynamics are affected by hydrological, hydrodynamic, and water quality factorsEbullient fluxes in shallow‐water and drawdown zones dominate atmospheric emissionOperations impact sediment CH4 consumption, ebullition, and atmospheric emission [ABSTRACT FROM AUTHOR]

Purpose: To examine whether or not folic acid (FA) supplementation may modify the relationships between duration or quality of sleep and gestational diabetes mellitus (GDM) risk. Methods: In a case–control study of patients with GDM and controls, mothers were interviewed face-to-face at enrollment. The Pittsburgh Sleep Quality Scale was used to assess duration and quality of sleep during early pregnancy, and information on FA supplementation and covariates was obtained using a semiquantitative questionnaire. Results: Among 396 patients with GDM and 904 controls, GDM risk increased by 328% and 148% among women with short (< 7 h) and long (≥ 9 h) sleep durations, respectively, compared to those averaging 7–8.9 h sleep. Mothers with poor sleep quality increased their GDM risk by an average of 75% (all p < 0.05). The effect of short sleep duration on GDM risk was much weaker among women with adequate FA supplementation (taking supplements containing ≥ 0.4 mg FA daily for each day of the first three months of pregnancy) than that among women with inadequate FA supplementation, with a p-value for interaction = 0.003. There were no significant effects of FA on links among long duration and poor quality of sleep with GDM risk. Conclusions: Sleep duration and quality in early gestation were related to increased GDM risks. FA supplementation may reduce GDM risk associated with short sleep duration. [ABSTRACT FROM AUTHOR]

Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In simulation experiments, DANTE successfully trains large-scale ONNs with 150 million neurons on ImageNet, previously unattainable, and accelerates training speeds significantly on the CIFAR-10 benchmark compared to single-neuron learning. In physical experiments, we develop a two-layer ONN system based on DANTE, which can effectively extract features to improve the classification of natural images. Optoelectronic neural networks are a promising avenue in AI computing for parallelization, power efficiency, and speed. Here, the authors present a dual-neuron optical-artificial learning approach for training large-scale diffractive neural networks, achieving VGG-level performance on ImageNet in simulation with a network that is 10 times larger than existing ones. [ABSTRACT FROM AUTHOR]

Treating chronic wounds requires transition from proinflammatory M1 to anti-inflammatory M2 dominant macrophages. Based on the role of tumor extracellular vesicles (tEVs) in regulating the phenotypic switching from M1 to M2 macrophages, we propose that tEVs may have a beneficial impact on alleviating the overactive inflammatory microenvironment associated with refractory wounds. On the other hand, as a nitric oxide donor, S-nitrosoglutathione (GSNO) can regulate inflammation, promote angiogenesis, enhance matrix deposition, and facilitate wound healing. In this study, a guar gum-based hydrogel with tEVs and GSNO was designed for the treatment of diabetic refractory wounds. This hybrid hydrogel was formed through the phenyl borate bonds, which can automatically disintegrate in response to the high reactive oxygen species (ROS) level at the site of refractory diabetic wounds, releasing tEVs and GSNO. We conducted a comprehensive evaluation of this hydrogel in vitro, which demonstrated excellent performance. Meanwhile, using a full-thickness excision model in diabetic mice, the wounds exposed to the therapeutic hydrogel healed completely within 21 days. The increased closure rate was associated with macrophage polarization and collagen deposition, accelerated fibroblast proliferation, and increased angiogenesis in the regenerating tissues. Therefore, this multifunctional hybrid hydrogel appears to be promising for clinical applications. [ABSTRACT FROM AUTHOR]

Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In simulation experiments, DANTE successfully trains large-scale ONNs with 150 million neurons on ImageNet, previously unattainable, and accelerates training speeds significantly on the CIFAR-10 benchmark compared to single-neuron learning. In physical experiments, we develop a two-layer ONN system based on DANTE, which can effectively extract features to improve the classification of natural images. Optoelectronic neural networks are a promising avenue in AI computing for parallelization, power efficiency, and speed. Here, the authors present a dual-neuron optical-artificial learning approach for training large-scale diffractive neural networks, achieving VGG-level performance on ImageNet in simulation with a network that is 10 times larger than existing ones. [ABSTRACT FROM AUTHOR]

In this study, quasi-two-dimensional BixSn1−xO2 (BTO) thin films were fabricated using a liquid metal transfer method. The ultraviolet (UV) photodetector based on BTO thin films was constructed, and the ultrahigh responsivity of 589 A/W was observed at 300 nm UV light illumination. Interestingly, by dropping ethanol during light-off period, the recovery time induced by the persistent photoconductivity (PPC) effect is reduced from 1.65 × 103 s to 5.71 s. Furthermore, the recovery time can also be reduced by dropping methanol, propylene glycol, NaNO2, and Na2SO3 after light termination. The working mechanisms are attributed to the rapid consumption of holes stored in BTO thin films by reaction with those solutions. This work demonstrates that the BTO thin films have potential applications in high-performance UV detectors and present an innovation route to weaken the PPC effects in semiconductors by introducing chemical liquids on their surface. [ABSTRACT FROM AUTHOR]

Motivated by applications to root-cause identification of faults in high-dimensional data streams that may have very limited samples after faults are detected, we consider multiple testing in models for multivariate statistical process control (SPC). With quick fault detection, only small portion of data streams being out-of-control (OC) can be assumed. It is a long standing problem to identify those OC data streams while controlling the number of false discoveries. It is challenging due to the limited number of OC samples after the termination of the process when faults are detected. Although several false discovery rate (FDR) controlling methods have been proposed, people may prefer other methods for quick detection. With a recently developed method called Knockoff filtering, we propose a knockoff procedure that can combine with other fault detection methods in the sense that the knockoff procedure does not change the stopping time, but may identify another set of faults to control FDR. A theorem for the FDR control of the proposed procedure is provided. Simulation studies show that the proposed procedure can control FDR while maintaining high power. We also illustrate the performance in an application to semiconductor manufacturing processes that motivated this development. [ABSTRACT FROM AUTHOR]

The hollow CeO2 (H‐CeO2) nanoparticles with mesoporous defects structure were prepared by a solid template method under ambient pressure and applied to catalyze the conversion of CO2 into dimethyl carbonate (DMC). The textural properties of H‐CeO2 were investigated by various characterization techniques. The results showed that H‐CeO2 have higher surface area, more mesoporous defects and higher surface oxygen vacancies concentration than those of conventional CeO2 with block morphology (C‐CeO2). Therefore, H‐CeO2 exhibited better catalytic performance for the conversion of CO2 into DMC. Additionally, the Ce(NO3)3 ⋅ 6H2O amount in preparation procedure was important for the formation of hollow structure and mesoporous defects. The DMC yield could reach 4.96 % on H‐CeO2‐1.2 (Ce(NO3)3 ⋅ 6H2O amount: 1.2 g) catalyst when the reaction was performed at 140 °C and 4.5 MPa for 4 h. This work proposed a facile strategy for designing CeO2 catalysts for the CO2 conversion by creating mesoporous defective structure. [ABSTRACT FROM AUTHOR]

Based on the social comparison model of competition, this research investigates the effect of the bottom elimination system on the level of individual competition, and the effect of the number of competitors and the elimination ratio under this system. Experiment 1 examined the effect of different incentives on individual competition levels through a simple text transcription task. Experiment 2 further tested the effect of the number of competitors and the elimination ratio on the level of individual competition by creating a bottom elimination scenario. The results of the two experiments showed that (1) Individuals exhibited higher levels of competition under the bottom elimination system than under the top reward system. (2) Under the bottom elimination system, N-effect is valid. The higher the number of competitors, the lower the level of competition. (3) The elimination ratio of the bottom elimination system buffers the N-effect, and the N-effect disappears under the condition of 20% elimination ratio. [ABSTRACT FROM AUTHOR]

The vacuum preloading combined flocculation and solidification method (VP-FSCM) has been proposed in recent years to increase the efficiency of high-water-content slurries. The characteristic of the strength development of cement-treated mud, especially in the early stage, has significant effects on the consolidation process in the application of the VP-FSCM. An appropriate model for strength development can help decide the design scheme for the combining method to ensure that a high stabilization efficiency of high-water-content slurries is achieved. In this work, laboratory tests are conducted to investigate the strength development of mud with low cement dosage from a very early stage using vane shear tests and unconfined compression tests. After integrating the strengths obtained from the two approaches, the strength development behavior is analyzed, and the offset time is valued by performing a back-analysis using a modified Gallavresi's equation. It is found that a linear model can be built between the offset time and the water–cement ratio for a series of samples with the same soil type and binder type. Based on the model, the actual and predicted strength values fit well with a correlation coefficient of above 0.998. The model also agrees well with the test data from other literature, thereby testifying to its accuracy. By implementing the proposed model using consolidation theory, the consolidation proceeding time in the combined improvement method is determined. [ABSTRACT FROM AUTHOR]

Semiconducting barium disilicide (BaSi2) is an emerging material for solar cell applications, and therefore, defect passivation is critical for improving its solar cell performance. Herein, the effect of atomic hydrogen (H) on the photoresponsivity of 500 nm-thick boron (B)-doped p-BaSi2 films was examined. The photoresponsivity reached ∼4 A/W (about twice the highest reported value for H-passivated undoped BaSi2 films) in B-doped p-BaSi2 films exposed to an atomic H supply for 5 − 10 min because of an increased minority-carrier lifetime, as measured by the microwave-detected photoconductivity decay. Furthermore, a ≥15 min atomic H supply was found to degrade photoresponsivity. Ab initio studies were used to interpret and understand experimental observations by analyzing states in the gap region, which can act as traps, in B-doped p-BaSi2 with H incorporation. The effect that atomic H had on the performance of B-doped p-BaSi2/n-Si heterojunction solar cells was also studied. The saturation current density was found to decrease by three orders of magnitude with the atomic H supply, and the conversion efficiency was increased up to 6.2%. Deep-level transient spectroscopy revealed a reduction of defect densities induced by the atomic H supply. Both experimental and theoretical viewpoints show that an atomic H supply is beneficial for BaSi2 solar cells. [ABSTRACT FROM AUTHOR]

To measure and analyze the evolution characteristics and influencing factors of China's industrial green development level is of great significance in achieving carbon neutrality goal. Based on the panel data from 2000 to 2018 of 30 provinces in China, this research uses the super slack-based measuring model and the Malmquist-Luenberger index to calculates China's industrial green total factor productivity and to describe its evolution characteristics using the kernel density function and moreover uses the Spatial Durbin model and the partial differential method to explores its main influencing factors. This study finds that China's overall industrial green development level is not high but shows an upward trend year by year; carbon emissions, fiscal decentralization, and urbanization are not conducive to improving the national industrial green development level, whereas economic development, foreign direct investment, industrial structure, and technological progress are positive contributors. At the same time, the level of economic development and technological progress have significant direct and spatial spillover effect. Our findings also provide some policy implications for improving China's industrial green development. [ABSTRACT FROM AUTHOR]

Photodetectors and imagers with short-wave infrared (SWIR) sensitivities at wavelengths beyond the bandgap of silicon (Eg ∼ 1.1 eV) are currently subject to a rapidly expanding application space. Despite continued technological advancement, linear dynamic range and high costs associated with processing III–V semiconductors still limit their widespread usage. Solution-processed colloidal quantum dots (CQDs) – with tunable bandgap and strong light-matter interaction – are highly suitable as infrared absorbers for cost-effective photodetection. Here, we demonstrate a SWIR photodiode architecture with n-type zinc oxide (ZnO) nanomembrane and p-type lead sulfide (PbS) CQD thin film vertically stacked together. In the PbS CQD/ZnO nanomembrane p-n photodiode, the incident SWIR photons can be absorbed in the PbS CQD film and the photoexcited electron-hole pairs are rapidly separated by the built-in electric field. The fabricated heterojunction p-n photodiodes show a broad short-circuit photocurrent response with a peak responsivity of ∼75 mA/W and a maximum linear dynamic range of ∼100 dB. Finally, to demonstrate the uniformity and scalability, 16 × 16 photodetectors arrays (256 pixels) have been realized showing a very uniform photocurrent response with Isc in the range of 100 nA. As the increased maturity of large-scale film fabrication of PbS CQD along with CMOS readout integrated circuit technology, focal plane arrays can be realized based on PbS CQD/ZnO nanomembrane heterostructures. [ABSTRACT FROM AUTHOR]

Freshwater reservoirs are widely recognized as methane (CH4) emission hotspots. Existing research has shown that temperature and hydrological conditions significantly affect wetland CH4 cycling processes. However, the feedback of the CH4 cycle to climate warming remains unclear for deep reservoirs where seasonal water thermal stratification exists. This study combined a reservoir CH4 cycling model and a Statistical DownScaling Model (SDSM) to evaluate reservoir CH4 cycling feedbacks under multiple climate change scenarios while accounting for hydrological uncertainty. Daily air temperatures in 2100 were predicted by the combination of the CanESM5 model and a SDSM. To address hydrological uncertainty, we selected three representative hydrological years (i.e., wet, normal, and dry) to create hydrological scenarios. Results showed that annual sediment CH4 production increased with warming, ranging 323.1–413.7 × 103 t C year−1 among multiple scenarios. Meanwhile, the CH4 oxidation percentage decreased with warming, which meant warming promoted sediment CH4 release non-linearly; 67.8–84.6% of sediment ebullient flux was ultimately emitted to the atmosphere (51.3–137.7 × 103 t C year−1), which showed ebullition was the dominant emission pathway. Higher air temperatures and drier conditions generally promote reservoir emissions. This study is helpful for predicting reservoir emissions while directing decision-making for reservoir sustainability. [ABSTRACT FROM AUTHOR]

Formaldehyde oxidation at room temperature with low-cost catalysts is one of the main development directions of environmental governance. Herein, we developed a low-cost and superior performance highly dispersed Ce on Na-ZSM-5 (Ce/Na-ZSM-5) catalyst, which is close to atomic dispersion for indoor formaldehyde (HCHO) oxidation at low temperature. The highly dispersed catalyst that was similar to atomic dispersion was characterized by EXAFS and AC HAADF-STEM. The optimal Ce/Na-ZSM-5 displays high HCHO removal performance (95%, at room temperature), as well as 100 h stable testing (∼90% HCHO removal). In addition, a reasonable reaction mechanism of the HCHO catalytic oxidation is proposed based on the in situ DRIFT spectra and DFT calculation. This work provides a new way to design an efficient catalyst for the complete oxidation of formaldehyde. [ABSTRACT FROM AUTHOR]

Epitaxy technology produces high-quality material building blocks that underpin various fields of applications. However, fundamental limitations exist for conventional epitaxy, such as the lattice matching constraints that have greatly narrowed down the choices of available epitaxial material combinations. Recent emerging epitaxy techniques such as remote and van der Waals epitaxy have shown exciting perspectives to overcome these limitations and provide freestanding nanomembranes for massive novel applications. Here, we review the mechanism and fundamentals for van der Waals and remote epitaxy to produce freestanding nanomembranes. Key benefits that are exclusive to these two growth strategies are comprehensively summarized. A number of original applications have also been discussed, highlighting the advantages of these freestanding films-based designs. Finally, we discuss the current limitations with possible solutions and potential future directions towards nanomembranes-based advanced heterogeneous integration. [ABSTRACT FROM AUTHOR]

Cervical squamous cell carcinoma (CSCC) is one of the leading causes of cancer death in women worldwide. Patients with advanced cervical carcinoma always have a poor prognosis once resistant to cisplatin due to the lack of effective treatment. It is urgent to investigate the molecular mechanisms of cisplatin resistance. Circular RNAs (circRNAs) are known to exert their regulatory functions in a series of malignancies. However, their effects on CSCC remain to be elucidated. Here, we found that cytoplasmic circARHGAP5, derived from second and third exons of the ARHGAP5 gene, was downregulated in cisplatin‐resistant tissues compared with normal cervix tissues and untreated cervical cancer tissues. In addition, experiments from overexpression/knockdown cell lines revealed that circARHGAP5 could inhibit cisplatin‐mediated cell apoptosis in CSCC cells both in vitro and in vivo. Mechanistically, circARHGAP5 interacted with AU‐rich element RNA‐binding protein (AUF1) directly. Overexpression of AUF1 could also inhibit cell apoptosis mediated by cisplatin. Furthermore, we detected the potential targets of AUF1 related to the apoptotic pathway and found that bcl‐2‐like protein 11 (BIM) was not only negatively regulated by AUF1 but positively regulated by circARHGAP5, which indicated that BIM mRNA might be degraded by AUF1 and thereby inhibited tumor cell apoptosis. Collectively, our data indicated that circARHGAP5 directly bound to AUF1 and prevented AUF1 from interacting with BIM mRNA, thereby playing a pivotal role in cisplatin resistance in CSCC. Our study provides insights into overcoming cancer resistance to cisplatin treatment. [ABSTRACT FROM AUTHOR]

To solverole inefficiency and complete autonomy issues for deflection of the vertical (DOV) determination in geomatics, navigation, and military weapon launching fields, a calculation method based on path compensation has been proposed. After conducting the discretization of the Vening-Meinesz formula in the spherical surface, a novel path gravity anomaly compensation based on the 'remove–restore' algorithm was formulated to satisfy the requirement of regional high-precision gravity anomaly data. Meanwhile, utilizing the European Improved Gravity model of Earth by New techniques (EIGEN-6c4), the DOV was calculated using the Vening-Meinesz formula based on the 'remove–restore' method, unlike the previous regional direct integral calculations. Finally, to verify the reliability and feasibility of the algorithm, the gravity measurement system, consisting of the high-precision gravimeter and strapdown inertial navigation system, was constructed to conduct the simulation and shipborne experiments. The simulation and experiment results of the proposed method show that the root mean squares, relative to the real values, of DOV calculation components, are 0.89″ and 1.67″, while standard deviation values of the DOV components are the same within 2″ in experiments, which satisfies the precision requirement of the DOV. [ABSTRACT FROM AUTHOR]

Taxi flow forecast is significant for planning transportation and allocating basic transportation resources. The flow forecast in the urban adjacent area is different from the fixed-point flow forecast. Their data are more complex and diverse, which make them more challenging to forecast. This paper introduces a deep spatial–temporal forecast (DeepSTF) model for the flow forecasting of urban adjacent area, which divides the urban into grids and makes it have a graph structure. The model builds a spatial–temporal calculation block, which uses graph convolutional network to extract spatial correlation feature and uses two-layer temporal convolutional networks to extract time-dependent feature. Based on the theory of dilation convolution and causal convolution, the model overcomes the under-fitting phenomenon of other models when calculating with rapidly changing data. In order to improve the accuracy of prediction, we take weather as an implicit factor and let it participate in the feature calculation process. A comparison experiment is set between our model and the seven existing traffic flow forecast models. The experimental results prove that the model has better the capabilities of long-term traffic prediction and performs well in various evaluation indicators. [ABSTRACT FROM AUTHOR]

Software defects, produced inevitably in software projects, seriously affect the efficiency of software testing and maintenance. An appealing solution is the software defect prediction (SDP) that has achieved good performance in many software projects. However, the difference between features and the difference of the same feature between training data and test data may degrade defect prediction performance if such differences violate the model's assumption. To address this issue, we propose a SDP method based on feature transfer learning (FTL), which performs a transformation sequence for each feature in order to map the original features to another feature space. Specifically, FTL first uses the reinforcement learning scheme that automatically learns a strategy for transferring the potential feature knowledge from the training data. Then, we use the learned feature knowledge to inspire the transformation of the test data. The classifier is trained by the transformed training data and predicts defects for transformed test data. We evaluate the validity of FTL on 43 projects from PROMISE and NASA MDP using three classifiers, logistic regression, random forest, and Naive Bayes (NB). Experimental results indicate that FTL is better than the original classifiers and has the best performance on the NB classifier. For PROMISE, after using FTL, the average results of F1‐score, AUC, MCC are 0.601, 0.757, and 0.350 respectively, which are 24.9%, 2.6%, and 16.7% higher than the original NB classifier results. The number of projects with improved performance accounts for 83.87%, 83.87%, and 64.52%. Similarly, FTL performs well on NASA MDP. Besides, compared with four feature engineering (FE) methods, FTL achieves an excellent improvement on most projects and the average performance is also better than or close to the FE methods. [ABSTRACT FROM AUTHOR]

The diagnosis of peripheral pulmonary lesions (PPLs) remains a challenge for physicians. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has been applied in the diagnosis of PPLs, but its diagnostic rate varies widely. The systematic review and meta-analysis was conducted to figure out the accuracy and safety of EBUS-TBNA in the diagnosis of PPLs. We searched the PubMed and Embase databases for relevant studies published from January 1, 2000 to December 30, 2021 and used PICO (Participants, Intervention, Comparison, and Outcome) to worked out the diagnostic rate of EBUS-TBNA in PPLs. Two reviewers independently performed the data extraction and assessed study quality. Statistical analysis was carried out via R software. In 7 studies of totally 510 patients of PPLs, the overall EBUS-TBNA diagnosis yield is 0.75 (95% CI 0.67–0.84) by the random effect model. EBUS-TBNA showed a higher accuracy of 0.64 (95% CI 0.53–0.74) compared to 0.46 (95% CI 0.19–0.72) of endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) when EBUS probe is adjacent to lesions. In the case of malignant lesions, the diagnostic rate of EBUS-TBNA is 0.79 (95% CI 0.72–0.88). Combined EBUS-TBNA with conventional bronchoscopy procedures showed the highest diagnostic yield (0.83 (95% CI 0.79–0.87)). Collectively, EBUS-TBNA should be performed firstly in patients with PPLs suspected to lung cancer especially when the EBUS probe was adjacent to the lesions. No serious procedure-related complications were observed. [ABSTRACT FROM AUTHOR]

Predicting urban travel demand is important in perceiving the future state of a city, deploying public transportation resources, and building intelligent cities. Influenced by multifarious factors, urban travel demand data have high-frequency noise and complex fluctuation patterns. Current studies have focused on predicting urban travel demand via various models. However, there is little work that comprehensively considers natural environmental factors and socioeconomic factors affecting urban travel demand. Some improvements are made in this work. First, multifarious influencing factors are taken into consideration. Second, a novel random forest-based method for influencing factor data preprocessing is introduced. Finally, this work proposes an urban travel demand prediction model considering influencing factors (UTDP-IF). A principal component analysis algorithm is used to extract the principal components of different influencing factors for avoiding multicollinearity. Based on four data sets, this work evaluates a UTDP-IF and compares it with some typical models. Compared with baselines, the root-mean-square error of the UTDF-IF is reduced by approximately 29.44% on average, which can perfectly predict urban travel demand. [ABSTRACT FROM AUTHOR]

In monocrystalline Si solar cells, pyramidal surface textures with the size of a few micrometers (micropyramids) are commonly applied for light in‐coupling and trapping. Recently, there is a growing need to reduce the size of pyramids below 1 μm. Herein, an original method to fabricate nanometer‐sized pyramids (nanopyramids) featuring an average size of ≈500 nm or even smaller by using a simple one‐step Ag‐assisted solution process is proposed. The texture size can be controlled by the concentration of AgNO3 in the alkaline etching solution where the generated Ag nanoparticles act as an etching mask as well as promoting the detachment of H2 bubbles from the reacting Si surface. This process enables the formation of uniformly distributed Si nanopyramids with a lower reflectance below 10% and a smaller etching margin less than 1.7 μm compared with the conventional micropyramids. Silicon heterojunction solar cells employing Si nanopyramids outperform those with the conventional micropyramids owing to the reduced optical reflectance while preserving excellent surface passivation and carrier transport properties. These results demonstrate the advantages of our method and the high potential of Si nanopyramids for various applications such as thin crystalline Si solar cells and perovskite/Si tandem solar cells. [ABSTRACT FROM AUTHOR]