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Atmospheric Hg(0) dry deposition is a vital process that significantly affects the global distribution and cycling of Hg. However, significant knowledge gaps and challenges remain in understanding atmospheric Hg(0) deposition and its subsequent post-deposition processes. Hg isotope fractionation has emerged as the most powerful tool for evaluating the impact of atmospheric Hg(0) deposition and unraveling key processes associated with it. By focusing on Hg isotope fractionation processes, Hg isotopic compositions, and influencing factors, this review presents current knowledge, recent advances, and new insights into atmospheric Hg(0) deposition and post-deposition processes over vegetation, soil, snow, and water surfaces. This review also points out the knowledge gaps pertaining to atmospheric Hg(0) deposition and highlights the need for further investigation into the associated processes, mechanisms, isotope fractionation, and modeling. Further research into Hg isotope fractionation in atmospheric Hg(0) deposition and post-deposition processes will advance source and process tracing, paleoclimate reconstruction, and the modeling of Hg isotope distribution on regional and global scales.

Abstract Quantum photonic integrated circuits are reshaping quantum networks and sensing by providing compact, efficient platforms for practical quantum applications. Despite continuous breakthroughs, integrating entangled registers into photonic devices on a CMOS-compatible platform presents significant challenges. Herein, we present single electron-nuclear spin entanglement and its integration into a silicon-carbide-on-insulator (SiCOI) waveguide. We demonstrate the successful generation of single divacancy electron spins and near-unity spin initialization of single 13C nuclear spins. Both single nuclear and electron spin can be coherently controlled and a maximally entangled state with a fidelity of 0.89 has been prepared under ambient conditions. Based on the nanoscale positioning techniques, the entangled quantum register has been further integrated into SiC photonic waveguides for the first time. We find that the intrinsic optical and spin characteristics of the register are well preserved and the fidelity of the entangled state remains as high as 0.88. Our findings highlight the promising prospects of the SiCOI platform as a compelling candidate for future scalable quantum photonic applications.

Abstract Purpose Vision-threatening diabetic retinopathy (VTDR) included severe non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR) and clinically significant diabetic macular edema (DME). To compare the axial length (AL) and assess its influence on VTDR across different ages. Methods A retrospective cohort study. Medical chart review was performed in 736 consecutive patients with VTDR. The patients were divided into young (≤ 45 years) and elderly group (> 45 years) based on their age at the diagnosis of VTDR. After at least one year of standardized treatments, all eligible patients were followed up. The main outcome measures included the presence of tractional retinal detachment (TRD) involving foveal, final best-corrected visual acuity (BCVA), the development of neovascular glaucoma (NVG), and recurrent vitreous hemorrhage (VH) post-vitrectomy. ALs were compared between two age groups. The impact of AL on clinical outcomes was determined by logistic analyses after controlling for systemic parameters. Results The study included 144 patients ≤ 45 years and 592 patients > 45 years. Young patients had significantly longer AL than elderly participants (23.9 mm vs 23.0 mm, p

Abstract Quantum metrology promises unprecedented precision of parameter estimation, but it is often vulnerable to noise. While significant efforts have been devoted to improving the metrology performance in Markovian environments, practical control schemes specifically designed for non-Markovian noises are much less investigated. Here, we propose two control-enhanced quantum metrology schemes that are suitable for tackling general non-Markovian noises described by noise channels or noise spectra. We conduct experiments to verify the efficacy of these schemes on a nuclear magnetic resonance system. The experimental results involving multiqubit probes show that the parameter estimation precision can be greatly improved, significantly surpassing the standard quantum limit, with our schemes. At present, non-Markovian noises are widely encountered on diverse quantum devices, the proposed schemes are relevant for realistic metrology applications on these platforms.

The objectives were to investigate effects of various concentrations of dietary vitamin D3 (VD3) or 25-hydroxycholecalciferol (25-OH-D3) on growth performance, VD3 status, calcium (Ca) metabolism, and tibia mineralization of starter male White Pekin ducks, and dietary requirements and relative bioavailability of these two compounds. One-day-old male White Pekin ducks (n = 624) were assigned to 13 dietary treatments, including 6 added VD3 concentrations (100, 200, 400, 800, 1200, and 2000 IU/kg), and 2 vitamin D3 sources (VD3 and 25-OH-D3), plus a negative control (no vitamin D3 supplementation) for 21 days. Each group had 6 replicate pens of 8 birds per pen. At 21 days of age, growth performance, carcass traits, plasma 25-OH-D3 and Ca content, and tibia mineralization were examined. There was a high incidence of leg disease and mortality in ducks fed the control diet; however, those negative effects were avoided by adequate VD3 or 25-OH-D3 supplementation. Ducks in the negative control group had the lowest body weight, average daily weight gain (ADG), average daily feed intake (ADFI), breast muscle percentage, plasma 25-OH-D3, plasma Ca, and tibia indices (weight, diameter, ash, density, tibia mineral, strength) (P < 0.05), with all these end points enhanced linearly or quadratically as dietary VD3 or 25-OH-D3 increased (P < 0.05). Furthermore, at 100 or 200 IU/kg, ducks fed 25-OH-D3 had greater body weight, ADG, ADFI, plasma Ca, plasma 25-OH-D3, tibia weight, tibia diameter, tibia ash than those fed VD3 (P < 0.05), indicating 25-OH-D3 was more effective in stimulating growth, Ca absorption, and tibia mineralization. For growth performance, breast muscle percentage, plasma Ca, and tibial indices of starter male Pekin ducks, the VD3 requirements were 281 to 633 IU/kg, whereas 25-OH-D3 requirements were 119 to 395 IU/kg. Based on slope ratio comparisons from multiple linear regressions of plasma 25-OH-D3, bioavailability of 25-OH-D3 was 186% relative to cholecalciferol.

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm—“ChatGPT + ML + Environment”, providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of “secondary training” for future application of “ChatGPT + ML + Environment”.

Being an essential component of organic chemistry, named reactions are indispensable in organic synthesis. More and more named reactions have been devised to create unique polymer backbones, which has significantly accelerated the creation of new polymer materials. Yet, the university chemical knowledge system is deficient in relevant introductions. The purpose of this study is to incorporate the specified reactions within the teaching of polymer chemistry. These reactions are divided into six categories: transition metal-catalyzed coupling reactions, condensation reactions, pericyclic reactions, multi-component reactions, free radical reactions, and other reactions. This work methodically covers these six types of reactions, which are critical for polymer backbone synthesis, and presents the practice in teaching. It can assist students to learn polymer chemistry in conjunction with organic chemistry and provide some reference for students and teachers.

Sodium-ion batteries (SIBs) have been proposed as a potential substitute for commercial lithium-ion batteries due to their excellent storage performance and cost-effectiveness. However, due to the substantial radius of sodium ions, there is an urgent need to develop anode materials with exemplary electrochemical characteristics, thereby enabling the fabrication of sodium-ion batteries with high energy density and rapid dynamics. Carbon materials are highly valued in the energy-storage field due to their diverse structures, low cost, and high reliability. This review comprehensively summarizes the typical structure; energy-storage mechanisms; and current development status of various carbon-based anode materials for SIBs, such as hard carbon, soft carbon, graphite, graphene, carbon nanotubes (CNTs), and porous carbon materials. This review also provides an overview of the current status and future development of related companies for sodium-ion batteries. Furthermore, it offers a summary and outlook on the challenges and opportunities associated with the design principles and large-scale production of carbon materials with high-energy-density requirements. This review offers an avenue for exploring outstanding improvement strategies for carbon materials, which can provide guidance for future application and research.

The evaluation of the focal field of Cassegrain antennas is crucial for the design and optimization of the complex feeds in quasi-optical systems. However, employing traditional physical optics methods generates high computational complexity and is inefficient. An accurate and efficient calculation method of Cassegrain antennas’ focal fields that involves a range of incidence angles is proposed, which integrates ray tracing and vector diffraction integration (RT-VDI) theories. It can calculate the focal field in any given or predefined incident direction, not limited to the case of axial incidence. In addition, the use of the equivalent parabolic theory greatly simplifies the process of integral calculation. Moreover, ray tracing and integration operations are executed upon the calculation of the reflector to further improve efficiency. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed method.

Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%.

With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm.

Lung nodule classification is crucial for the diagnosis and treatment of lung diseases. However, selecting appropriate metrics to evaluate classifier performance is challenging, due to the prevalence of negative samples over positive ones, resulting in imbalanced datasets. This imbalance often necessitates the augmentation of positive samples to train powerful models effectively. Furthermore, specific medical tasks require tailored augmentation methods, the effectiveness of which merits further exploration based on task objectives. This study conducted a detailed analysis of commonly used metrics in lung nodule detection, examining their characteristics and selecting suitable metrics based on this analysis and our experimental findings. The selected metrics were then applied to assessing different combinations of image augmentation techniques for nodule classification. Ultimately, the most effective metric was identified, leading to the determination of the most advantageous augmentation method combinations.

PurposeTo investigate the prevalence of diabetic retinopathy (DR) and vision-threatening DR (VTDR) in patients with type 2 diabetes mellitus (T2DM) stratified by the duration of diabetes and to identify the clinical variations and risk factors for VTDR occurring at different stages of T2DM.MethodsThis was a retrospective comparative study. Patients were divided into short- (≤3 years), intermediate- (3–7 years), and long-duration (>7 years) groups. All patients were followed-up for DR and VTDR development. Risk factors were explored using logistic regression analysis.ResultsA total of,2961 patients were included; among them, 1,036 (35.0%) patients developed DR, and 293 (9.9%) had VTDR. The frequency of VTDR in patients who developed DR in the short-duration group was significantly higher than that in the intermediate-duration group (25.7% vs. 15.0%; p = 0.019), but comparable with that of the long-duration group (25.7% vs. 31.8%; p = 0.138). Patients who developed VTDR within the first 3 years of T2DM were more likely to have a family history of diabetes (p = 0.024), had higher glycated hemoglobin (p = 0.025), were males (p = 0.042), and were notably older at the onset of diabetes (p

Among existing approaches to holonomic quantum computing, the adiabatic holonomic quantum gates (HQGs) suffer errors due to decoherence, while the non-adiabatic HQGs either require additional Hilbert spaces or are difficult to scale. Here, we report a systematic, scalable approach based on dynamical invariants to realize HQGs without using additional Hilbert spaces. While presenting the theoretical framework of our approach, we design and experimentally evaluate single-qubit and two-qubits HQGs for the nuclear magnetic resonance system. The single-qubit gates acquire average fidelity 0.9972 by randomized benchmarking, and the controlled-NOT gate acquires fidelity 0.9782 by quantum process tomography. Our approach is also platform-independent, and thus may open a way to large-scale holonomic quantum computation.

Abstract Background Diabetic retinopathy (DR), a specific neuron-vascular complication of diabetes, is a major cause of vision loss among middle-aged people worldwide, and the number of DR patients will increase with the increasing incidence of diabetes. At present, it is limited in difficult detection in the early stages, limited treatment and unsatisfactory treatment effects in the advanced stages. Main body The pathogenesis of DR is complicated and involves epigenetic modifications, oxidative stress, inflammation and neovascularization. These factors influence each other and jointly promote the development of DR. DNA methylation is the most studied epigenetic modification, which has been a key role in the regulation of gene expression and the occurrence and development of DR. Thus, this review investigates the relationship between DNA methylation and other complex pathological processes in the development of DR. From the perspective of DNA methylation, this review provides basic insights into potential biomarkers for diagnosis, preventable risk factors, and novel targets for treatment. Conclusion DNA methylation plays an indispensable role in DR and may serve as a prospective biomarker of this blinding disease in its relatively early stages. In combination with inhibitors of DNA methyltransferases can be a potential approach to delay or even prevent patients from getting advanced stages of DR.

Topological quantum computation (TQC) is one of the most striking architectures that can realize fault-tolerant quantum computers. In TQC, the logical space and the quantum gates are topologically protected, i.e., robust against local disturbances. The topological protection, however, requires complicated lattice models and hard-to-manipulate dynamics; even the simplest system that can realize universal TQC—the Fibonacci anyon system—lacks a physical realization, let alone braiding the non-Abelian anyons. Here, we propose a disk model that can simulate the Fibonacci anyon system and construct the topologically protected logical spaces with the Fibonacci anyons. Via braiding the Fibonacci anyons, we can implement universal quantum gates on the logical space. Our disk model merely requires two physical qubits to realize three Fibonacci anyons at the boundary. By 15 sequential braiding operations, we construct a topologically protected Hadamard gate, which is to date the least-resource requirement for TQC. To showcase, we implement a topological Hadamard gate with two nuclear spin qubits, which reaches 97.18% fidelity by randomized benchmarking. We further prove by experiment that the logical space and Hadamard gate are topologically protected: local disturbances due to thermal fluctuations result in a global phase only. As a platform-independent proposal, our work is a proof of principle of TQC and paves the way toward fault-tolerant quantum computation.

Abstract Background Diabetic retinopathy (DR) as a severe diabetic complication contributes to blindness. The increased permeability of retinal capillary endothelial cells (RCECs) as well as the production of inflammatory markers are closely related to DR occurrence. We recently revealed that TRIM46 promotes high glucose (HG)-caused ferroptosis in human RCECs (HRCECs). The current study aims to explore the molecular mechanism of how TRIM46 plays its role in DR progression. Methods Western blot was utilized to determine protein expression. The cell counting kit-8 assay was used to observe cell viability. The permeability of the cell layer was determined by measuring the transepithelial electrical resistance and fluorescein isothiocyanate (FITC)-dextran leak. Enzyme-linked immunosorbent assay was used to quantify the protein level of pro-inflammatory cytokines and co-immunoprecipitation was employed to verify the relationship between TRIM46 and IκBα. Results HG dramatically upregulated TRIM46 protein expression in a dose-dependent way. Silencing TRIM46 effectively reversed HG-induced cell growth inhibition, cell cycle arrest, hyper permeability and pro-inflammatory cytokines secretion in HRCECs, while overexpression of TRIM46 exhibited an opposite effect. Furthermore, TRIM46 was able to interact with IκBα and promote the ubiquitination and degradation of IκBα. IκBα overexpression recovered the effects of TRIM46 overexpression in HRCECs. Furthermore, inhibiting the activation of NF-κB partially recovered HG-induced HRCEC injury, whereas TRIM46 overexpression reversed these effects. Conclusion This study demonstrates that TRIM46 interacts with IκBα to activate the NF-κB signaling pathway, thereby enhancing cell proliferation inhibition, hyper permeability and the inflammatory response of HRCECs in a HG state.

Abstract Noise is ubiquitous in real quantum systems, leading to non-Hermitian quantum dynamics, and may affect the fundamental states of matter. Here we report in an experiment a quantum simulation of the two-dimensional non-Hermitian quantum anomalous Hall (QAH) model using the nuclear magnetic resonance processor. Unlike the usual experiments using auxiliary qubits, we develop a stochastic average approach based on the stochastic Schrödinger equation to realize the non-Hermitian dissipative quantum dynamics, which has advantages in saving the quantum simulation sources and simplifying the implementation of quantum gates. We demonstrate the stability of dynamical topology against weak noise and observe two types of dynamical topological transitions driven by strong noise. Moreover, a region where the emergent topology is always robust regardless of the noise strength is observed. Our work shows a feasible quantum simulation approach for dissipative quantum dynamics with stochastic Schrödinger equation and opens a route to investigate non-Hermitian dynamical topological physics.

Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distribution (OOD) data and prompt human intervention, a critical competency. While several frameworks for OOD detection have been introduced and achieved remarkable results, most state-of-the-art (SOTA) models rely on supervised learning with annotated data for their training. However, acquiring labeled data can be a demanding, time-consuming or, in some cases, an infeasible task. Consequently, unsupervised learning has gained substantial traction and has made noteworthy advancements. It empowers models to undergo training solely on unlabeled data while still achieving comparable or even superior performance compared to supervised alternatives. Among the array of unsupervised methods, contrastive learning has asserted its effectiveness in feature extraction for a variety of downstream tasks. Conversely, auto-encoders are extensively employed to acquire indispensable representations that faithfully reconstruct input data. In this study, we introduce a novel approach that amalgamates contrastive learning with auto-encoders for OOD detection using unlabeled data. Contrastive learning diligently tightens the grouping of in-distribution data while meticulously segregating OOD data, and the auto-encoder augments the feature space with increased refinement. Within this framework, data undergo implicit classification into in-distribution and OOD categories with a notable degree of precision. Our experimental findings manifest that this method surpasses most of the existing detectors reliant on unlabeled data or even labeled data. By incorporating an auto-encoder into an unsupervised learning framework and training it on the CIFAR-100 dataset, our model enhances the detection rate of unsupervised learning methods by an average of 5.8%. Moreover, it outperforms the supervised-based OOD detector by an average margin of 11%.

The current study was to investigate the modulatory effects of total dietary fiber (TDF) levels on cecal morphology and the response of microbiota to maintain gut health for duck growth. A total of 192 14-day-old male white Pekin ducks were randomly allocated to three dietary groups and fed diets, containing 12.4, 14.7, and 16.2% TDF, respectively, until 35 days under the quantitative feed intake. Each dietary group consisted of eight replicate cages of eight birds. The results revealed that 14.7 and 16.2% TDF groups significantly promoted growth performance and improved villus height, the ratio of villus to crypt, muscle layer thickness, and goblet cells per villus of cecum in ducks. qPCR results showed that the transcriptional expression of Claudin-1, Muc2, IGF-1, and SLC16A1 was significantly upregulated in cecum in 14.7 and 16.2% TDF groups. Meanwhile, the concentration of IGF-1 in circulating was significantly increased in 14.7 and 16.2% TDF groups while that of DAO was significantly decreased in 16.2% TDF group. Furthermore, the concentrations of butyrate, isobutyrate, valerate, and isovalerate in cecum were conspicuously improved in 14.7 and 16.2% TDF groups while that of propionate was significantly decreased. In addition, the concentrations of butyrate, isobutyrate, valerate, and isovalerate in cecum presented negative correlations with the concentration of DAO in circulating. 16S rRNA gene sequencing results showed that the 14.7% TDF group importantly elevated the microbial richness. Simultaneously, butyrate-producing bacteria like the family Lachnospiraceae, Oscillospiraceae, and Erysipelatoclostridiaceae were enriched as biomarkers in the 16.2% TDF group. Correlation network analysis revealed that the associations between specific bacteria and short-chain fatty acids (SCFAs) induced by different TDF levels, and the correlations among bacteria were also witnessed. For example, the genus Monoglobus and CHKCI002 showed a positive correlation with butyrate, and there was a positively coexistent association between Monoglobus and CHKCI002. In summary, these data revealed that increasing the TDF level could enhance the cecal morphology and drive cecal species-specific of SCFAs in ducks.

Microglial activation and melatonin protection have been reported in diabetic retinopathy (DR). Whether melatonin could regulate microglia to protect the inner blood–retinal barrier (iBRB) remains unknown. In this study, the role of microglia in iBRB breakdown and the mechanisms of melatonin’s regulation on microglia were explored. In diabetic rat retinas, activated microglia proliferated and migrated from the inner retina to the outer retina, accompanied by the obvious morphological changes. Meanwhile, significant leakage of albumin was evidenced at the site of close interaction between activated microglia and the damaged pericytes and endothelial cells. In vitro, inflammation-related cytokines, such as tumor necrosis factor-α (TNF-α), inducible nitric oxide synthase (iNOS), interleukin (IL)-1β, and arginase-1 (Arg-1), were increased significantly in CoCl2-treated BV2 cells. The supernatant derived from CoCl2-treated BV2 cells significantly decreased the cell viability and disrupted the junctional proteins in both pericytes and endothelial cells, resulting in severe leakage. Melatonin suppressed the microglial overactivation, i.e., decreasing the cell number and promoting its anti-inflammatory properties in diabetic rat retinas. Moreover, the leakage of iBRB was alleviated and the pericyte coverage was restored after melatonin treatment. In vitro, when treated with melatonin in CoCl2-treated BV2 cells, the inflammatory factors were decreased, while the anti-inflammatory factors were increased, further reducing the pericyte loss and increasing the tight junctions. Melatonin deactivated microglia via inhibition of PI3K/Akt/Stat3/NF-κB signaling pathways, thus maintaining the integrity of iBRB. The present data support a causal role for activated microglia in iBRB breakdown and highlight the therapeutic potential of melatonin in the treatment of DR by regulating microglia.

Diabetic retinopathy (DR), with increasing incidence, is the major cause of vision loss and blindness worldwide in working-age adults. Diabetic macular edema (DME) remains the main cause of vision impairment in diabetic patients, with its pathogenesis still not completely elucidated. Vascular endothelial growth factor (VEGF) plays a pivotal role in the pathogenesis of DR and DME. Currently, intravitreal injection of anti-VEGF agents remains as the first-line therapy in DME treatment due to the superior anatomic and functional outcomes. However, some patients do not respond satisfactorily to anti-VEGF injections. More than 30% patients still exist with persistent DME even after regular intravitreal injection for at least 4 injections within 24 weeks, suggesting other pathogenic factors, beyond VEGF, might contribute to the pathogenesis of DME. Recent advances showed nearly all the retinal cells are involved in DR and DME, including breakdown of blood-retinal barrier (BRB), drainage dysfunction of Müller glia and retinal pigment epithelium (RPE), involvement of inflammation, oxidative stress, and neurodegeneration, all complicating the pathogenesis of DME. The profound understanding of the changes in proteomics and metabolomics helps improve the elucidation of the pathogenesis of DR and DME and leads to the identification of novel targets, biomarkers and potential therapeutic strategies for DME treatment. The present review aimed to summarize the current understanding of DME, the involved molecular mechanisms, and the changes in proteomics and metabolomics, thus to propose the potential therapeutic recommendations for personalized treatment of DME.

Lithium-metal batteries (LMBs) are regarded as the most promising candidate for practical applications in portable electronic devices and electric vehicles because of their high capacity and energy density. However, the uncontrollable growth of lithium dendrite reduces its cycling ability and even causes a severe safety concern, which impedes the development of the technology. Although great efforts have been devoted to solving the lithium dendrite issue in recent years, the contradiction between the high cost of thin Li foil and the severe safety hazard of excess Li still exists. This is precisely the factor that inspired the development of anode-free lithium-metal batteries (AFLMBs). Compared to lithium-metal batteries, AFLMBs with a zero-excess Li anode possess an incredible, conceivable, and specific energy. Additionally, because the use of metal lithium is limited, the battery manufacturing will be safer and simpler, leading to a significant decrease in cost. However, comprehensive reviews on anode-free batteries are rare. Therefore, in this review, we aim to explain the essential development factors influencing the cycle life, energy density, cost, and working mechanism of anode-free batteries. We summarize different strategies to improve the cycling stability of AFLMBs, and we discuss the application of anode-free electrodes in other electrochemical energy storage systems. Moreover, it is believed that the combination of modification techniques, including electrolytes and current collectors, and the application protocols will be the most important solution for future anode-free batteries.