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This article presents the design methodology, fabrication process, and experiment evaluation of a bionic vector turbulence sensor with a combined stress-dispersed crossbeam and bullet-shaped package structure for turbulence measurements in harsh marine environments. The sensor is primarily designed based on the optimized reliability using the finite-element method. Specifically, a stress-dispersed cilium-beam sensitive structure with excellent mechanical robustness is developed by optimizing the stress concentration regions on the beam. Compared to traditional sensitive elements, a significant decrease in concentrated stress can be achieved, and the failure limit considerably increases. The crossbeam microstructure is fabricated based on the microelectromechanical systems (MEMSs) technology, and piezoresistors are formed on the beams. The bullet-shaped package structure achieves high-pressure resistance by employing a double water-tightness approach. In particular, the design of open front fairwater limits excessive displacement of cilium to avoid structural destruction due to overload. The calibration results indicate that the sensitivity of the sensor reaches $1.06\times 10^{-{2}}$ ( $\text {V}\cdot \text {m}\cdot \text {s}^{{2}}$ )/kg. A systematic evaluation of the sensor’s marine environment adaptability, reliability, and stability is conducted to validate the robust performance of the designed sensor. Furthermore, a field test is performed in a real environment. The obtained results suggest that the reported sensor exhibits excellent environmental adaptability and reliability. The success of this investigation paves the way for the technology maturity and engineering application of the MEMS turbulence sensor.

Dual-band electrochromic (EC) materials are expected to be utilized as building materials for energy saving, but their cycle stability is still an obstacle. Here, two D–A conjugated polymers, PDPPCz36 and PDPPCz27, based on diketopyrrolopyrrole and carbazole linked with different sites, are synthesized. Both of them exhibit dual-band EC behaviors with a dark blue color in the neutral state and high absorption in the near-infrared (NIR) region when oxidized. However, PDPPCz36 exhibits better cycle stability and a shorter response time than PDPPCz27. In the visible (VIS) region, the PDPPCz36 film exhibits an initial light modulation range (ΔT) of 43.0% and that of the PDPPCz27 film is 41.3%. After 100 cycles of redox, the ΔTof the PDPPCz36 film declines by 32%, while that of PDPPCz27 attenuates by more than 50%. A similar tendency is evident in the NIR region. Moreover, PDPPCz36 shows subsecond colored switch times both in the VIS (0.4 s) and NIR (0.7 s) regions, while those of PDPPCz27 are 2.1 and 1.6 s, respectively. Further research suggests that 3,6-linked carbazoles in PDPPCz36 simultaneously inhibit the side reaction and film aggregation, which leads to better redox stability and shorter response time in both EC tests and devices.

Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels, neglecting the widely-used multiple-input multiple-output (MIMO) channels, which hinders the application into practical systems. One common solution to combat MIMO fading is to utilize feedback MIMO channel state information (CSI). In this paper, we incorporate MIMO CSI into system designs from a new perspective and propose the learnable CSI fusion semantic communication (LCFSC) framework, where CSI is treated as side information by the semantic extractor to enhance the semantic coding. To avoid feature fusion due to abrupt combination of CSI with features, we present a non-invasive CSI fusion multi-head attention module inside the Swin Transformer. With the learned attention masking map determined by both source and channel states, more robust attention distribution could be generated. Furthermore, the percentage of mask elements could be flexibly adjusted by the learnable mask ratio, which is produced based on the conditional variational interference in an unsupervised manner. In this way, CSI-aware semantic coding is achieved through learnable CSI fusion masking. Experiment results testify the superiority of LCFSC over traditional schemes and state-of-the-art Swin Transformer-based semantic communication frameworks in MIMO fading channels.

Sodium bis(fluorosulfonyl)imide (NaFSI) electrolytes are renowned for their superior physicochemical and electrochemical properties, making them ideal for high-performance sodium-ion batteries (SIBs). However, severe oxidative dissolution of aluminum current collectors (commonly known as Al corrosion) in NaFSI-based electrolytes occurs at high potentials. To address this challenge, aiming to understand the Al corrosion mechanism and develop strategies to inhibit corrosion, we propose dual-salt electrolytes using 0.8 mol L–1(M) NaFSI and 0.2 M of a second fluorine-containing sodium salt dissolved in EC/PC solutions (1:1, v/v) to construct an insoluble deposits layer on the Al. Dual-salt electrolytes adopting a second sodium salt capable of passivating the Al collector have been extensively investigated through various techniques, such as cyclic voltammetry, scanning electron microscopy, chronoamperometry, X-ray photoelectron spectroscopy, and charge–discharge tests. Our findings demonstrate that introducing sodium difluoro(oxalato)borate (NaDFOB) into the NaFSI electrolytes inhibits Al corrosion, which is attributed to the formation of insoluble deposits of Al–F (AlF3) and B–F containing polymers. Moreover, the capacity retention of Na||Na3V2(PO4)3(NVP) cells using the NaFSI-NaDFOB dual-salt electrolyte reaches 99.2% along with a Coulombic efficiency over 99.3% at a 1 C rate after 200 cycles. This research provides a practical solution for passivating Al collectors in SIBs with NaFSI electrolytes and promotes the development of sodium batteries with long calendar lifetimes.

Layered Division Multiplexing (LDM) is a Power-based Non-Orthogonal Multiplexing (P-NOM) technique that has been implemented in the Advanced Television System Committee (ATSC) 3.0 terrestrial TV physical layer to effectively multiplex services with different robustness and data rate requirements. As communication systems quickly evolve, the services to be delivered are becoming more diverse and versatile. Up to now, the LDM system adopted in the terrestrial TV system uses a uniform injection level for the lower-level (or Layer 2) signal injection. This paper investigates the non-uniform injection level LDM (NULDM). The proposed technique can explore the Unequal Error Protection (UEP) property of Low-Density Parity-Check (LDPC) codes and the flexible power allocation nature of the NULDM to improve the system performance and spectrum efficiency. NULDM enables the seamless integration of broadcast/multicast and unicast services in one RF channel, where the unicast signal can assign different resources (power, frequency, and time) based on the UE distance and service requirements. Meanwhile, more power could be allocated to improve the upper layer (or Layer 1) broadcast and datacast services. To make better use of the UEP property of LDPC codes in NULDM, the extended Gaussian mixture approximation (EGMA) method is used to design bit interleaving patterns. Additionally, inspired by the channel order of polar codes, this paper proposes an LDPC sub-block interleaving order (SBIO) scheme that performs similarly to the EGMA interleaving model, while better adapting to the diverse needs of proposed mixed service delivery scenarios for convergence of broadband wireless communications and broadcasting systems.

With the arrival of the 6G era, wireless communication networks will face increased pressure due to diversified service traffic with ultra-large bandwidth, ultra-low latency, and massive connections, making it difficult to guarantee quality of service. However, broadcasting can realize wide-area coverage with lower physical transmission resource occupancy. Therefore, the convergence of broadcasting and 6G networks can promote the evolution and upgrade of traditional broadcasting services towards flexibility, dynamics, and personalization, and at the same time, can effectively alleviate the data congestion in mobile communication networks. In this paper, we firstly introduce the three typical application scenarios of broadcasting and 6G convergence in the future, and summarize the vital technologies and challenges in constructing the converged network. On this basis, we propose a broadcasting and 6G converged network architecture and a next-generation 6G broadcasting core network architecture, and finally introduce the typical collaboration modes of the converged network.

With the escalating prevalence of datacasting, live streaming and high-quality video consumption on mobile devices, there arises an increasing demand for a cost-effective and reliable approach to transmit large volumes of such content to extensive audiences. While broadband mobile networks can increase capacity through denser base stations and higher frequencies, the linear pace of facility development makes it difficult to match the non-linear growth of the service throughput. Terrestrial Broadcast has proven itself to be significantly more efficient in transmitting popular video streams to mobile devices over a large area. However, due to its downlink-only nature, it falls short of delivering consistently reliable services. Hence, the convergence of terrestrial broadcast and broadband mobile networks has resurfaced as a pertinent topic for consideration. In this paper, terrestrial broadcast is adopted as the main pipe to transmit streaming services to mobile phones, with a 5th generation mobile communications (5G) new radio (NR) mobile carrier employed to provide complementary packet loss retransmission service, ensuring a seamless service experience. First, a cross-standard packet retransmission (CPR) scheme is proposed based on 5G broadcast and 5G NR Systems. Corresponding protocols and schemes are introduced, and a prototype system is realized. CPR is able to support delay-insensitive datacasting services very well, yet its higher layer convergence poses challenges for supporting delay-sensitive real-time services. To address this, a MAC-layer homogeneous packet retransmission (HPR) scheme is proposed. The basic principle is to utilize the carrier aggregation mechanism of 5G, modifying the protocols to enable one carrier to simulate broadcast while maintaining unicast in another carrier. In HPR, packet retransmission can be done at the MAC layer, reducing the retransmission delay to within 5 microseconds. Simulation and trial results are presented based on the proposed schemes.

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models. We also theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.

Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation patterns exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter remarkably changes, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. During the early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entirety of eastern China, thus contributing to more precipitation there. During the late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in differing water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly the opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central-eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China.

Graphite exhibits crystal anisotropy, which impedes the mass transfer of ion intercalation and extraction processes in Li-ion batteries. Herein, a dual-shock chemical strategy has been developed to synthesize the carbon anode. This approach comprised two key phases: (1) a thermal shock utilizing ultrahigh temperature (3228 K) can thermodynamically facilitate graphitization; (2) a mechanical shock (21.64 MPa) disrupting the π–π interactions in the aromatic chains of carbon can result in hybrid-structured carbon composed of crystalline and amorphous carbon. The optimized carbon (DSC-200–0.3) demonstrates a capacity of 208.61 mAh/g at a 10C rate, with a significant enhancement comparing with 15 mAh/g of the original graphite. Impressively, it maintains 81.06% capacity even after 3000 charge–discharge cycles. Dynamic process analysis reveals that this superior rate performance is attributed to a larger interlayer spacing facilitating ion transport comparing with the original graphite, disordered amorphous carbon for additional lithium storage sites, and crystallized carbon for enhanced charge transfer. The dual-shock chemical approach offers a cost-effective and efficient method to rapidly produce hybrid-structured carbon anodes, enabling 10C fast charging capabilities in lithium-ion batteries.

Layered double hydroxides (LDHs), especially those containing nickel (Ni), are increasingly recognized for their potential in photo(-/)electrocatalytic water oxidation due to the abundant availability of Ni, their corrosion resistance, and their minimal toxicity. This review provides a comprehensive examination of Ni-based LDHs in electrocatalytic (EC), photocatalytic (PC), and photoelectrocatalytic (PEC) water oxidation processes. The review delves into the operational principles, highlighting similarities and distinctions as well as the benefits and limitations associated with each method of water oxidation. It includes a detailed discussion on the synthesis of monolayer, ultrathin, and bulk Ni-based LDHs, focusing on the merits and drawbacks inherent to each synthesis approach. Regarding the EC oxygen evolution reaction (OER), strategies to improve catalytic performance and insights into the structural evolution of Ni-based LDHs during the electrocatalytic process are summarized. Furthermore, the review extensively covers the advancements in Ni-based LDHs for PEC OER, including an analysis of semiconductors paired with Ni-based LDHs to form photoanodes, with a focus on their enhanced activity, stability, and underlying mechanisms facilitated by LDHs. The review concludes by addressing the challenges and prospects in the development of innovative Ni-based LDH catalysts for practical applications. The comprehensive insights provided in this paper will not only stimulate further research but also engage the scientific community, thus driving the field of photo(-/)electrocatalytic water oxidation forward.

Two-photon photodynamic therapy (TP-PDT) has garnered significant attention because of its excellent depth of tissue penetration and high spatiotemporal selectivity. However, the limited targeting ability and oxygen dependency of photosensitizers (PSs) significantly hinder the effectiveness of photodynamic therapy in hypoxic tumor treatment. Herein, we designed and synthesized two lipid droplet (LD)-targeted two-photon PSs (TBPCP and TBCP) by reducing benzene rings to achieve “acceptor planarization”. Notably, acceptor planarization not only enhanced the intramolecular charge transfer but also transferred the photochemical reaction from type II (TBPCP) to type I (TBCP). Under the irradiation of 940 nm femtosecond pulsed laser, TBPCP and TBCP showed bright two-photon-excited fluorescence and excellent LD targeting in living cells. Comparing TBPCP (type II PS), the outstanding TP-PDT efficacy of TBCP (type I PS) under hypoxic conditions could be obtained in both cellular experiments and multicellular tumor spheroids (MCTS) model. Additionally, both TBPCP and TBCP could induce the lipid peroxidation in the type I or type II PDT due to the location of LD, depleting GSH and inactivating GPX4 to induce non-programmed ferroptosis in cells.

The introduction of NCIL simultaneously achieves a rapid Zn2+desolvation, homogeneous Zn2+spatial distribution and the construction of ion-screening interface, thus contributing to a kinetically enhanced and dendrites-free galvanization process.

The long-term maintenance of leukaemia stem cells (LSCs) is responsible for the high degree of malignancy in MLL(mixed-lineage leukaemia) rearranged acute myeloid leukaemia (AML). The DNA damage response (DDR) and DOT1L/H3K79me pathways are required to maintain LSCs in MLLr-AML, but little is known about their interplay. This study revealed that the DDR enzyme ATM regulates the maintenance of LSCs in MLLr-AML with a sequential protein-posttranslational-modification manner via CBP-DOT1L. We identified the phosphorylation of CBP by ATM, which confers the stability of CBP by preventing its proteasomal degradation, and characterised the acetylation of DOT1L by CBP, which mediates the high level of H3K79me2 for the expression of leukaemia genes in MLLr-AML. In addition, we revealed that the regulation of CBP-DOT1L axis in MLLr-AML by ATM was independent of DNA damage activation. Our findings provide insight into the signalling pathways involoved in MLLr-AML and broaden the understanding of the role of DDR enzymes beyond processing DNA damage, as well as identigying them as potent cancer targets.

The importance of selective synthesis of high-value-added chemicals from renewable resources is paramount but remains a crucial challenge in organic synthesis and chemical reformation. Herein, we report the selective photosynthesis of C–C coupling products and carbonyl compounds from biomass-derived alcohols. The key to ensuring high end-to-end selectivity is the modulation of the reactivity of ketyl radical (*RCHOH) intermediates by employing different metal co-catalysts (Au, Pt, Pd, Ru) supported on Cd0.6Zn0.4S solid solution (CZS) photocatalysts. In particular, the C–C coupling product, hydrobenzion, and fully oxidized benzaldehyde were obtained from benzyl alcohol with high selectivity (> 98%) over Au-CZS and Ru-CZS, respectively. Combined experimental and theoretical analyses demonstrated that the affinity of *RCHOH for the surface of metals governs their subsequent transformations, in which weak and strong radical adsorption on Au and Ru results in C–C coupling products and carbonyl compounds, respectively.

This paper studies the synchronization and control of chaotic systems while proposing a novel chaotic-based path-tracking application for mobile robots (MRs) to ensure their safety and security. In security-based applications that use MRs, such as patrol MRs, the path of the MRs must be complex enough to prevent easy prediction. Multiple chaotic systems with a chaotic switching mechanism are introduced for secure path planning. The main challenges are that the dynamics of MRs are entirely unknown. The modeled dynamics of the MRs are unreliable in practice due to a broad range of uncertainties related to the parameters, operating conditions, environmental impacts, time delays, unmodeled frictions, noisy sensors, and faulty actuators. Also, the chaotic switching of reference signals between chaotic signals imposes a high dynamic perturbation. The main novelties are as follows: (1) a strong secure path is introduced for MRs. (2) A powerful fractional-order predictive controller using type-3 (T3) fuzzy-logic systems (FLSs) is developed. (3) The estimation and prediction errors of T3-FLSs are compensated by a designed parallel compensator. (4) T3-FLSs are tuned online, such that stability is ensured, and prediction accuracy is guaranteed. (5) The suggested scheme is implemented on a real-world MR, and the results demonstrate the feasibility and accuracy of the proposed method. Also, in several simulations, the efficacy of the introduced controller is examined.

Hepatocellular carcinoma (HCC) stands as the fifth most prevalent malignant tumor on a global scale and presents as the second leading cause of cancer-related mortality. DNA damage-based radiotherapy (RT) plays a pivotal role in the treatment of HCC. Nevertheless, radioresistance remains a primary factor contributing to the failure of radiation therapy in HCC patients. In this study, we investigated the functional role of transketolase (TKT) in the repair of DNA double-strand breaks (DSBs) in HCC. Our research unveiled that TKT is involved in DSB repair, and its depletion significantly reduces both non-homologous end joining (NHEJ) and homologous recombination (HR)-mediated DSB repair. Mechanistically, TKT interacts with PARP1 in a DNA damage-dependent manner. Furthermore, TKT undergoes PARylation by PARP1, resulting in the inhibition of its enzymatic activity, and TKT can enhance the auto-PARylation of PARP1 in response to DSBs in HCC. The depletion of TKT effectively mitigates the radioresistance of HCC, both in vitro and in mouse xenograft models. Moreover, high TKT expression confers resistance of RT in clinical HCC patients, establishing TKT as a marker for assessing the response of HCC patients who received cancer RT. In summary, our findings reveal a novel mechanism by which TKT contributes to the radioresistance of HCC. Overall, we identify the TKT-PARP1 axis as a promising potential therapeutic target for improving RT outcomes in HCC.

Thiazole scaffold-based small molecules exhibit a range of biological activities and play important roles in drug discovery. Based on bioinformatics analysis, a putative biosynthetic gene cluster (BGC) for thiazole-containing compounds was identified from Streptomycessp. SCSIO 40020. Heterologous expression of this BGC led to the production of eight new thiazole-containing compounds, grisechelins E, F, and I–N (1, 2, 5–10), and two quinoline derivatives, grisechelins G and H (3and 4). The structures of 1–10, including their absolute configurations, were elucidated by HRESIMS, NMR spectroscopic data, ECD calculations, and single-crystal X-ray diffraction analysis. Grisechelin F (2) is a unique derivative, distinguished by the presence of a salicylic acid moiety. The biosynthetic pathway for 2was proposed based on bioinformatics analysis and in vivogene knockout experiments. Grisechelin E (1) displayed moderate antimycobacterial activity against Mycobacterium tuberculosisH37Ra (MIC of 8 μg mL–1).

For a multiple-input multiple-output (MIMO) system with unknown channel state information (CSI), a novel low-density parity check (LDPC)-coded transmission (LCT) scheme with joint pilot and data channel estimation is proposed. To fine-tune the CSI, a method based on the constraints introduced by the coded data from an LDPC code is designed such that the MIMO detector exploits the fine-tuned CSI. For reducing the computational burden, a coordinate ascent algorithm is employed along with several approximation methods, effectively reducing the required times of MIMO detection and computational complexity to achieve a satisfying performance. Simulation results utilizing WiMAX standard LDPC codes and quadrature phase-shift keying (QPSK) modulation demonstrate gains of up to 1.3 dB at a frame error rate (FER) of 10−4 compared to pilot-assisted transmission (PAT) over Rayleigh block-fading channels.

The knowledge of channel covariance matrices is crucial to the design of intelligent reflecting surface (IRS) assisted communication. However, channel covariance matrices may change suddenly in practice. This letter focuses on the detection of the above change in IRS-assisted communication. Specifically, we consider the uplink communication system consisting of a single-antenna user (UE), an IRS, and a multi-antenna base station (BS). We first categorize two types of channel covariance matrix changes based on their impact on system design: Type I change, which denotes the change in the BS receive covariance matrix, and Type II change, which denotes the change in the IRS transmit/receive covariance matrix. Secondly, a powerful method is proposed to detect whether a Type I change occurs, a Type II change occurs, or no change occurs. The effectiveness of our proposed scheme is verified by numerical results.

With the rapid development of the metaverse, AR/VR technology and consumer electronics have emerged as crucial drivers for future virtual experiences. In this context, realistic 3D scene modeling and rendering has become particularly critical. Neural Radiance Fields (NeRF), as a deep learning-based method, have made significant progress in the reconstruction and rendering of real-world 3D scenes. However, NeRF still faces challenges in efficiently handling high-frequency information within the object, leading to issues such as blurry details and artifacts. To address this issue and provide more immersive virtual experiences, we propose Vivid-NeRF, which offers benefits to both metaverse and AR/VR technologies. Vivid-NeRF extracts information at different frequencies from the image and fully exploits the high-frequency information in the 3D feature generation process to obtain more realistic details. In addition, we propose frequency-based sampling to increase the sampling of high-frequency components. Finally, we merge frequency information with viewpoint features obtained through frequency-based sampling to enhance the model’s capability in expressing scene details. With these improvements, Vivid-NeRF significantly reduces surface blur and accurately captures and reproduces the smooth surface appearance. We conduct experiments on the Blender and Shiny-Blender datasets. Experimental results demonstrate that Vivid-NeRF achieves PSNR of 35.00 and 34.01, SSIM of 0.976 and 0.970, and LPIPS of 0.033 and 0.064. Both quantitative and qualitative assessments demonstrate that our approach outperforms the previous state-of-the-art (SOTA) ABLE-NeRF.

The ternary strategy has been widely applied and recognized to be a valid strategy to enhance the organic photovoltaics’ (OPVs) performance. Here, a new fused-ring electron acceptor, BTP-PIO, is designed and synthesized, whose ending groups were replaced by a phthalimide-based group (2-butylcyclopenta[f]isoindole-1,3,5,7(2H,6H)-tetraone) from traditional 2-(3-oxo-2,3-dihydro-1H-inden-1-ylidene)malononitrile. The phthalimide-based ending groups endow BTP-PIO with the highest lowest unoccupied molecular orbital (LUMO) level and wider band gap than those of Y6. The ternary device based on PM6:Y6 with BTP-PIO as a guest electron acceptor achieved an elevated open-circuit voltage (VOC) of 0.848 V, a short-circuit current density (JSC) of 27.31 mA cm–2, and a fill factor (FF) of 73.9%, generating a remarkable power conversion efficiency (PCE) of 17.10%, which is superior to the PM6:Y6 binary device of 16.08%. The ternary device exhibited improved charge transfer, suppressed carrier recombination, and lower energy loss. BTP-PIO exhibited a good miscibility with Y6, and an alloy phase between BTP-PIO and Y6 was formed in the ternary bulk heterojunction, leading to better phase separation and molecular packing. This research reveals that ending group modification of Y6 derivatives is a feasible way to produce highly efficient ternary devices.

Lake sediments connection to the biogeochemical cycling of phosphorus (P) and carbon (C) influences streamwater quality. However, it is unclear whether and how the type of sediment controls P and C cycling in water. Here, the adsorption behavior of montmorillonite (Mt) with different interlayer cations (Na+, Ca2+, or Fe3+) on dissolved organic matter (DOM) and P was investigated to understand the role of Mt in regulating the organic carbon-to-phosphate (OC/P) ratio within freshwater systems. The adsorption capacity of Fe–Mt for P was 3.2-fold higher than that of Ca–Mt, while it was 1/3 lower for DOM. This dissimilarity in adsorption led to an increased OC/P in Fe–Mt-dominated water and a decreased OC/P in Ca–Mt-dominated water. Moreover, an in situ atomic force microscope and high-resolution mass spectrometry revealed molecular fractionation mechanisms and adsorptive processes. It was observed that DOM inhibited the nucleation and crystallization processes of P on the Mt surface, and P affected the binding energy of DOM on Mt through competitive adsorption, thereby governing the interfacial P/DOM dynamics on Mt substrates at a molecular level. These findings have important implications for water quality management, by highlighting the role of clay minerals as nutrient sinks and providing new strategies for controlling P and C dynamics in freshwater systems.

Chemical methods for the extraction and refinement of technologically critical rare earth elements (REEs) are energy-intensive, hazardous, and environmentally destructive. Current biobased extraction systems rely on extremophilic organisms and generate many of the same detrimental effects as chemical methodologies. The mesophilic methylotrophic bacterium Methylobacterium extorquensAM1 was previously shown to grow using electronic waste by naturally acquiring REEs to power methanol metabolism. Here we show that growth using electronic waste as a sole REE source is scalable up to 10 L with consistent metal yields without the use of harsh acids or high temperatures. The addition of organic acids increases REE leaching in a nonspecific manner. REE-specific bioleaching can be engineered through the overproduction of REE-binding ligands (called lanthanophores) and pyrroloquinoline quinone. REE bioaccumulation increases with the leachate concentration and is highly specific. REEs are stored intracellularly in polyphosphate granules, and genetic engineering to eliminate exopolyphosphatase activity increases metal accumulation, confirming the link between phosphate metabolism and biological REE use. Finally, we report the innate ability of M. extorquensto grow using other complex REE sources, including pulverized smartphones, demonstrating the flexibility and potential for use as a recovery platform for these critical metals.

Wireless capsule robot technology is promising because it is minimally invasive. Magnetically guided capsule robots, in particular, have garnered significant attention because they allow for noninvasive or less invasive remote control of endoscopes within the body. However, miniaturizing capsule robots remains a challenge, especially for applications within the urinary system. This article proposed a novel teardrop-shaped magnetically guided capsule robot, which has several salient features. First, a coupled design strategy was implemented for the actuator and body, reducing the overall size of the device. Second, a bio-inspired teardrop shape was utilized in the design to minimize resistance (RES) as the robot moves within the body, with the design process supported by the finite element analysis, regression model, and the Taguchi robust design technique. Third, a special 3-D printing method was employed to fabricate a prototype of the robot, taking advantage of its capability to print micro/nanometer features. An in vitro experiment was conducted to validate the effectiveness and potential of the capsule robot system.

The Yangtze River basin (YRB) experienced a record-breaking mei-yu season in June–July 2020. This unique long-lasting extreme event and its origin have attracted considerable attention. Previous studies have suggested that the Indian Ocean (IO) SST forcing and soil moisture anomaly over the Indochina Peninsula (ICP) were responsible for this unexpected event. However, the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event, especially their linkage with atmospheric circulation changes, remain unclear. By using observations and numerical simulations, this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020. Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds. The intensification of the warm condition further magnified the land thermal effects, which in turn facilitated the westward extension of the western North Pacific subtropical high (WNPSH) in June–July. The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB, thereby contributing to the 2020 mei-yu. In contrast, the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture. The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model. Their synergistic impacts yield a notable 32% increase in YRB precipitation. Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.

Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging information with neighbor agents through V2X communication. However, most existing work assume ideal communication among agents, ignoring the significant and common interruption issues caused by imperfect V2X communication, where cooperation agents can not receive cooperative messages successfully and thus fail to achieve cooperative perception, leading to safety risks. To fully reap the benefits of cooperative perception in practice, we propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP), a cooperative perception system robust to communication interruption for V2X communication-aided autonomous driving, which leverages historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information. To further improve recovery performance, we adopt a knowledge distillation framework to give explicit and direct supervision to the prediction model and a curriculum learning strategy to stabilize the training of the model. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception. V2X-INCOP outperforms state-of-the-art cooperative perception methods and has a cooperative perception gain up to 14.06%, 13.9%, and 12.07% over individual perception on average of different packet drop rates on OPV2V, V2X-Sim, and Dair-V2X datasets, respectively.

Opinion dynamics (OD) studies the members’ opinion formation process to a specific issue in a group interacting environment. The existing literature seldom considers the implicit information of their opinions and neighboring members’ influence during iterations. This article first proposes a method to describe the members’ mental processes of changing opinions based on the support information that consists of the trust relationship among the neighboring members and the difference among the neighboring members’ opinions, and this method is named the support-based method. Then, a convergence condition based on the network structure is given, and an analysis of the convergence of the model is also provided. Further, inspired by the idea of the proportional-integral-derivative (PID) controller, we propose a new method to capture the opinion changes in three areas: 1) the past cumulative performance; 2) the current performance; and 3) the future trend of the performance, and this method is named PID-inspired method. Numerical experiments are provided to validate the effectiveness of the support-based method and the PID-inspired method; specifically, the PID-inspired method has a higher-convergence rate than the support-based method.

This article investigates a robust cooperative path following control algorithm for an unmanned surface vessel and unmanned aerial vehicles (USV-UAVs) that releases the design complexity and command transmission requirements for the potential significance to implement a maritime square search mission. The latter is a classical search pattern suggested by the international aeronautical and maritime search and rescue (IAMSAR) manual. To program the reference signals for the USV-UAVs simultaneously, the reference routes are programmed that uses a virtual ship and a virtual aerial. Therein, the information of the virtual aerials can be mapped from a virtual ship. Further, the desired attitude angles are derived under the cooperative virtual guidance framework. For a control part, a novel threshold rule of the dynamic event-triggered mechanism is reconstructed according to output errors to avoid the artificial parameter tuning and reduce the data transmission load from the controllers to the actuators. Besides, a L-function is designed to eliminate an effect caused by the model nonlinearities and disturbances. This ensures a lower design complexity of the control system. The stability of the proposed control algorithm is proved via the Lyapunov theorem. Finally, the effectiveness and the reasonable are evaluated on the simulation platform.

The rapid development of flexible triboelectric nanogenerators (TENGs) has become an alternative to batteries for wearable devices. Stretchable, multifunctional, and low-cost are the primary development directions for these wearable devices based on TENG. Herein, a stretchable triboelectric generator with coplanar integration was designed for energy harvesting and force sensing. The industrial conductive silicone and silicone were used to fabricate the TENG with a thickness of less than 0.9 mm. When the elongation was less than 150%, TENG exhibited excellent linear characteristics in the resistance-tensile strain correspondence, and the coefficient of determination was 0.99. This stretchable TENG with a sufficient contact area of 9 cm2could generate a short-circuit current of 2 µA when it was in contact with the skin. Lastly, an intelligent tension monitoring wearable device that can effectively measure the tensile force was developed. Such a stretchable, coplanar integration-based, and low-cost wearable device has excellent applicability in wearable electronics.

Radiofrequency ablation (RFA) is an energy-based tumor ablation modality that heats tissues by delivering radiofrequency current. The tissue properties of both electrical and thermal conductivities dominate the efficiency of RFA. This study aims to investigate the potential of electroporation (EP) pretreatment to enhance RFA by increasing the electrical and thermal conductivities of tissues. A rabbit liver model was used in an in vivo experiment to do the following: 1) characterize the change of electrical conductivity and thermal conductivity after various EP protocols and 2) explore the RFA enhancement with previous EP by comparing the ablation zone among the groups of different ablation protocols. Furthermore, a computer model, validated by the in vivo experiment, was built to study the enhancing effect of previous EP on RFA treatments. Compared with untreated tissues, the maximum electrical and thermal conductivities of liver tissues treated by EP treatments could be obtained at the electric field strengths of 2500 and 1500 V/cm, respectively, which increased by 16.54% (from $0.29~\pm ~0.11$ to $0.34~\pm ~0.01$ S/m) and 65.17% [from $0.71~\pm ~0.03$ to $1.18~\pm ~0.12$ W/(m $\cdot $ K)], respectively. The areas of ablation zone in the in vivo experiment were $56.35~\pm ~5.93$ , $63.52~\pm ~13.73$ , and $80.82~\pm ~11.91$ mm2 following the EP of 0, 375, and 750 V/cm, respectively. Correspondingly, the ablation zone in the computer simulation was increased to 73.26 mm2 with an EP of 750 V/cm compared to 50.97 mm2 with no EP. The study concludes that the ablation effectiveness of RFA can be enhanced by EP treatment just prior to RFA, and the enhancement positively correlates with the applied electric field strengths.

Multi-access edge computing (MEC) is a promising solution to supporting resource-intensive applications on mobile devices (MDs), which enables computation offloading from MDs to edge servers at their proximities. However, the quality of the communication links and the limited communication and computing resources significantly impact the performance of MEC systems. In this paper, we leverage the emerging reconfigurable intelligent surfaces (RISs) to assist the computation offloading and balance the computing workloads in a multi-server MEC system with limited communication and computing resources. Specifically, when a nearby edge server is overwhelmed by multiple computing tasks, some MDs can be redirected to potentially distant but lighter-loaded edge servers by employing passive beamforming enabled by RISs. Thus, to maximize the task completion rate, we formulate a joint optimization problem for user association, passive beamforming at RISs, receive beamforming at BSs, and computing resource allocation on edge servers. Since the problem is a mixed integer nonlinear programming (MINLP), which is challenging to solve, we first decompose it into two tractable subproblems through the block coordinate descent (BCD) technique and then solve them by the penalty dual decomposition (PDD) method and a swap matching-based algorithm, respectively. Numerical results demonstrate that the task completion rate can be significantly increased by incorporating RISs into multi-server MEC systems. Besides, the proposed algorithms outperform other benchmark schemes in terms of both the task completion rate and the design complexity.

In this article, a prescribed performance path following control algorithm is presented for underactuated surface vessels (USV) that uses an output-based threshold rule and a shifting function to execute parallel (also called lawn mowing) maritime search missions. The latter consist of classical search patterns suggested by the international aeronautical and maritime search and rescue (IAMSAR) manual. The proposed scheme comprises a guidance and a control module. In the guidance module, a limited boundary-based guidance principle is developed with a position threshold rule to avoid continuous computation of heading reference signals. Associated with the guidance signal, a novel dynamic event-triggered mechanism is derived by designing an output-based dynamic rule without fixed parameters, which has the potential to reduce unnecessary communication resource usage between the controllers and the actuator systems when compared with existing techniques. A shifting function is introduced to ensure that the path following error is within a prescribed region. This will ensure proper balance between communication burden and tracking accuracy in the closed-loop system. Semi-global uniform ultimate bounded (SGUUB) stability of the proposed control system is guaranteed using Lyapunov theory. The advantages and effectiveness of the proposal algorithm are shown via simulations.

Quartz resonant pressure sensors have emerged as a promising technology for precise pressure measurements in a variety of applications such as aerospace, meteorological observation, and energy exploitation. These sensors are inherently compatible with digital techniques and provide high accuracy, high resolution, and good long-term stability over a wide pressure range. These excellent features can be tentatively attributed to the mechanical and electrical properties of quartz crystals. However, there are no reviews that delve into the mechanisms underlying the superior performance of such sensors, leading to a bottleneck in technological development. Accordingly, this article reviews the key physical properties and effects of crystalline material, which have driven sensor technology advances. In particular, it is gathered that crystal cuts significantly influence the operation and performance of the sensors. These cuts can be classified into three categories based on the sensitivity to the measurement: pressure-sensitive, temperature-sensitive, and frequency-reference cuts. We also summarize the mechanisms that govern the optimization of the crystal cuts and the effect of the combinations of different cuts on performance improvement. In addition, the unique physical effects of quartz crystals are presented, and their role in sensor technology innovation is discussed in detail. Next, the different quartz resonant pressure-sensing technologies available are classified according to the acoustic wave transmission method: surface acoustic wave, vibrating beam, and vibrating diaphragm types. We conclude this article with an analysis of state-of-the-art sensor technologies and a discussion on the status of the sensor industry and the latest technology trends.

In this study, we combined electrochemical impedance spectroscopy (EIS) and artificial neural network (ANN) to predict the ablation zone of irreversible electroporation (IRE) in a heterogeneous plant model. The heterogeneous plant model was built by implanting an exotic material with a different electrical conductivity (copper or wood) into potato cubes. For each heterogeneous model, 55 IRE trials were performed with the pulse strength of 300–1300 V and the pulse number of 30, 60, or 90 ( $100 \mu \text{s}$ in the pulsewidth and the frequency of 1 Hz) for different positions of exotic implants. The ANN for each model was trained, tested, and validated by a total of 165 experimental data with five inputs (pulse strength, pulse number, implant $x$ -/ $y$ -axis values, and impedance variation parameter) and four outputs (ablation area, major axis length (MaAL), minor axis length (MiAL), and ablation boundary in the first quadrant). Both the experiment and simulation results showed that the two implants with different electrical conductivities could distort the electric field distribution in the plant model. This study concludes that the method combining ANN and EIS can be used to predict the ablation zone of heterogeneous IRE with acceptable accuracy ( $>$ 90%), which might bring a hint to the rapid monitoring of IRE in the treatment of tumors.

The broadcast-cellular converged network presents a promising architecture for meeting the increasing demand for high-bandwidth and low-latency services while alleviating traffic burdens. In this paper, we consider a broadcast-cellular converged network to provide both live streaming and on-demand services. In this converged network, the broadcast network is responsible for delivering live streaming videos to all users within its coverage area, while the cellular network transmits on-demand videos to intended users. In cases where users cannot successfully receive broadcasting signals, the cellular network also provides live streaming services through network-level traffic offloading. We first analyze and model the live streaming and on-demand video services based on a real-world dataset. The analytical expressions for the average waiting time, system capacity, and power consumption of the proposed broadcast-cellular converged network are then derived based on the $M/G/N/\infty $ queue model. The simulation parameters are based on the actual station deployment scenario in Beijing. We discover that the broadcast-cellular converged network enhances system capacity, reduces average waiting time, and saves energy when the number of users requiring live streaming videos exceeds a certain threshold. We also derive the broadcast on-off threshold, which is independent of the density of on-demand users, but rather dependent on the density of live streaming users. Extensive simulation results demonstrate that compared to the cellular-only network, the proposed converged network can effectively support a larger number of on-demand users and live streaming users simultaneously.

Introduction: Human respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infection, especially in children and older people. However, no effective treatment is currently available. Type I interferons (IFNs) are a group of cytokines that help regulate the activity of the immune system. GB05, human IFNα1b inhalation solution, was developed under US Food and Drug Administration (FDA) standard guidelines to combat RSV infection. This randomized, double-blind, placebo-controlled, dose-escalation phase I trial evaluated the safety, tolerability, and pharmacokinetics of nebulized GB05. Methods: A total of 35 eligible healthy Chinese adult volunteers were enrolled in this study. In the single ascending dose (SAD) study, volunteers were randomized into 0.2, 0.6, 1.2, and 1.8 million IU of GB05 or placebo. In the multiple ascending dose (MAD) study, volunteers received 1.2 or 1.8 million IU of GB05 or placebo for four consecutive days. Safety, tolerability, immunogenicity, and plasma pharmacokinetics were assessed for all groups. Results: All adverse events were mild or moderate and resolved spontaneously. The most common adverse event was decreased white blood cell count (8.6% in SAD and 10% in MAD). No serious adverse events, deaths, or adverse events that reached the termination criteria occurred during the study. In SAD, the maximum concentration and area under the curve increased across the dose range of 1.2–1.8 million IU in a non-linear relationship. The maximum plasma concentration after GB05 nebulization (1.06 IU/ml in the 1.8 million IU group) reflected a low concentration in the blood, suggesting a better lung uptake of GB05 and reduced incidence or risks of adverse events. In MAD, a steady state was reached after continuous administrations of twice daily for 3 days. Conclusions: Overall, nebulized GB05 exhibited satisfactory safety, tolerability, and favorable pharmacokinetic (PK) profiles in healthy adult volunteers, supporting further clinical investigation in patients infected with respiratory syncytial virus. Clinical Trial Registration: ClinicalTrials.gov Identifier NCT06277167.

This study proposes a novel control scheme for simultaneously tracking and stabilizing nonholonomic wheeled mobile robots (NWMRs) subject to actuator and sensor faults, measurement errors, uncertain dynamics, and time-varying slippage/skid disturbances. To this end, a nonlinear model based on a type-3 (T3) fuzzy logic system (FLS) is developed for NWMR tracking and stabilization. Furthermore, a nonlinear model predictive controller (NMPC) is designed analytically without employing iterative computations, thus achieving fast performance. A new approach of type-3 nonsingleton fuzzification is introduced to handle measurement errors. Additionally, faults in the actuators and sensors are detected by a supervisory scheme and eliminated by a devised compensator. Finally, extensive simulations and experimental validations are conducted to further verify the effectiveness of the proposed scheme, along with a comparative analysis of several benchmarking methods.

This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users’ channel information and propose a modified $K $ -medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

Regional sustainable development necessitates a holistic understanding of spatiotemporal variations in ecosystem carbon storage (ECS), particularly in ecologically sensitive areas with arid and semi-arid climate. In this study, we calculated the ECS in the Ningxia Section of Yellow River Basin, China from 1985 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model based on land use data. We further predicted the spatial distribution of ECS in 2050 under four land use scenarios: natural development scenario (NDS), ecological protection scenario (EPS), cultivated land protection scenario (CPS), and urban development scenario (UDS) using the patch-generating land use simulation (PLUS) model, and quantified the influences of natural and human factors on the spatial differentiation of ECS using the geographical detector (Geodetector). Results showed that the total ECS of the study area initially increased from 1985 until reaching a peak at 402.36×106t in 2010, followed by a decreasing trend to 2050. The spatial distribution of ECS was characterized by high values in the eastern and southern parts of the study area, and low values in the western and northern parts. Between 1985 and 2020, land use changes occurred mainly through the expansion of cultivated land, woodland, and construction land at the expense of unused land. The total ECS in 2050 under different land use scenarios (ranked as EPS>CPS>NDS>UDS) would be lower than that in 2020. Nighttime light was the largest contributor to the spatial differentiation of ECS, with soil type and annual mean temperature being the major natural driving factors. Findings of this study could provide guidance on the ecological construction and high-quality development in arid and semi-arid areas.

Recent advancements in SDRTV-to-HDRTV conversion have yielded impressive results in reconstructing high dynamic range television (HDRTV) videos from standard dynamic range television (SDRTV) videos. However, the practical applications of these techniques are limited for ultra-high definition (UHD) video systems due to their high computational and memory costs. In this paper, we propose EffiHDR, an efficient framework primarily operating in the downsampled space, effectively reducing the computational and memory demands. Our framework comprises a real-time SDRTV-to-HDRTV Reconstruction model and a plug-and-play HDRTV Enhancement model. The SDRTV-to-HDRTV Reconstruction model learns affine transformation coefficients instead of directly predicting output pixels to preserve high-frequency information and mitigate information loss caused by downsampling. It decomposes SDRTV-to-HDR mapping into pixel intensity-dependent and local-dependent affine transformations. The pixel intensity-dependent transformation leverages global contexts and pixel intensity conditions to transform SDRTV pixels to the HDRTV domain. The local-dependent transformation predicts affine coefficients based on local contexts, further enhancing dynamic range, local contrast, and color tone. Additionally, we introduce a plug-and-play HDRTV Enhancement model based on an efficient Transformer-based U-net, which enhances luminance and color details in challenging recovery scenarios. Experimental results demonstrate that our SDRTV-to-HDRTV Reconstruction model achieves real-time 4K conversion with impressive performance. When combined with the HDRTV Enhancement model, our approach outperforms state-of-the-art methods in performance and efficiency.

The visual quality of 3D-synthesized videos is closely related to the development and broadcasting of immersive media such as free-viewpoint videos and six degrees of freedom navigation. Therefore, studying the 3D-Synthesized video quality assessment is helpful to promote the popularity of immersive media applications. Inspired by the texture compression, depth compression and virtual view synthesis polluting the visual quality of 3D-synthesized videos at pixel-, structure- and content-levels, this paper proposes a Multi-Level 3D-Synthesized Video Quality Assessment algorithm, namely ML-SVQA, which consists of a quality feature perception module and a quality feature regression module. Specifically, the quality feature perception module firstly extracts motion vector fields of the 3D-synthesized video at pixel-, structure- and content-levels by combining the perception mechanism of human visual system. Then, the quality feature perception module measures the temporal flicker distortion intensity in the no-reference environment by calculating the self-similarity of adjacent motion vector fields. Finally, the quality feature regression module uses the machine learning algorithm to learn the mapping of the developed quality features to the quality score. Experiments constructed on the public IRCCyN/IVC and SIAT synthesized video datasets show that our ML-SVQA is more effective than state-of-the-art image/video quality assessment methods in evaluating the quality of 3D-Synthesized videos.

This letter aims to detect abrupt changes in the base station to intelligent reflecting surface (BS-IRS) channel for IRS-assisted communications. Specifically, we first propose a centralized channel change detector, where the BS enables a fusion change detection based on the log-likelihood ratios (LLRs) obtained from all the user equipments. Next, to curtail the feedback overhead, we propose a decentralized channel change detector, where each UE independently detects channel changes and conveys a 1-bit outcome signal to the BS enabling a change detection. Numerical results validate that the proposed change detectors can promptly detect the change with low false alarm rate.

The effects of four copper catalysts with different surface states on the catalytic selectivity and mechanism of carbon dioxide reduction and the synthesis strategy of the catalysts were reviewed.

Straw-related carbon (C) dynamics are central for C accrual in agro-ecosystems and should be assessed by investigating their decomposition and soil organic carbon (SOC) priming effects. Our understanding of biotic and abiotic mechanisms underpinning these two C processes, however, is still not sufficiently profound. Soils that had received organic and mineral fertilizers for 26 years were sampled for a 28 day incubation experiment to assess 13C-labeled straw decomposition and SOC priming effects. On the basis of analyzing physicochemical properties, fungal taxonomic (MiSeq sequencing) and functional (metagenomics) guilds, we quantified the contributions of biotic and abiotic attributes to straw decomposition and SOC priming. Here, we propose two distinct mechanisms underlying straw decomposition and SOC priming in agriculture soils: (i) accelerated straw mineralization in manure-treated soils was mainly driven by biotic forces, while (ii) larger SOC priming in NPK-amended soils was through abiotic regulation.

The electrochemical 5-hydroxymethylfurfural oxidation reaction (HMFOR) has been regarded as a viable alternative to sustainable biomass valorization. However, the transformation of the catalysts under harsh electrooxidation conditions remains controversial. Herein, we confirm the self-construction of cuprous sulfide nanosheets (Cu2S NSs) into sulfate-terminated copper oxide nanorods (CuO-SO42–NRs) during the first-cycle of the HMFOR, which achieves a near-quantitative synthesis of 2,5-furandicarboxylic acid (FDCA) with a >99.9% yield and faradaic efficiency without deactivation in 15 successive cycles. Electrochemical impedance spectroscopies confirm that the surface SO42–effectively reduces the onset potential for HMFOR, while in situRaman spectroscopies identify a reversible transformation from CuII-O to CuIII-OOH in HMFOR. Furthermore, density functional theory calculations reveal that the surface SO42–weakens the Cu–OH bonds in CuOOH to promote the rate-determining step of its coupling with the C atom in HMF-H* resulting from HMF hydrogenation, which synergistically enhances the catalytic activity of CuO-SO42–NRs toward HMF-to-FDCA conversion.

All-perovskite tandem solar cells hold great promise in surpassing the Shockley–Queisser limit for single-junction solar cells1–3. However, the practical use of these cells is currently hampered by the subpar performance and stability issues associated with mixed tin–lead (Sn–Pb) narrow-bandgap perovskite subcells in all-perovskite tandems4–7. In this study, we focus on the narrow-bandgap subcells and develop an all-in-one doping strategy for them. We introduce aspartate hydrochloride (AspCl) into both the bottom poly(3,4-ethylene dioxythiophene)–poly(styrene sulfonate) and bulk perovskite layers, followed by another AspCl posttreatment. We show that a single AspCl additive can effectively passivate defects, reduce Sn4+impurities and shift the Fermi energy level. Additionally, the strong molecular bonding of AspCl–Sn/Pb iodide and AspCl–AspCl can strengthen the structure and thereby improve the stability of Sn–Pb perovskites. Ultimately, the implementation of AspCl doping in Sn–Pb perovskite solar cells yielded power conversion efficiencies of 22.46% for single-junction cells and 27.84% (27.62% stabilized and 27.34% certified) for tandems with 95% retention after being stored in an N2-filled glovebox for 2,000 h. These results suggest that all-in-one AspCl doping is a favourable strategy for enhancing the efficiency and stability of single-junction Sn–Pb perovskite solar cells and their tandems.

For real-time classification of rock-masses in hard-rock tunnels, quick determination of the rock lithology on the tunnel face during construction is essential. Motivated by current breakthroughs in artificial intelligence technology in machine vision, a new automatic detection approach for classifying tunnel lithology based on tunnel face images was developed. The method benefits from residual learning for training a deep convolutional neural network (DCNN), and a multi-scale dilated convolutional attention block is proposed. The block with different dilation rates can provide various receptive fields, and thus it can extract multi-scale features. Moreover, the attention mechanism is utilized to select the salient features adaptively and further improve the performance of the model. In this study, an initial image data set made up of photographs of tunnel faces consisting of basalt, granite, siltstone, and tuff was first collected. After classifying and enhancing the training, validation, and testing data sets, a new image data set was generated. A comparison of the experimental findings demonstrated that the suggested approach outperforms previous classifiers in terms of various indicators, including accuracy, precision, recall, F1-score, and computing time. Finally, a visualization analysis was performed to explain the process of the network in the classification of tunnel lithology through feature extraction. Overall, this study demonstrates the potential of using artificial intelligence methods for in siturock lithology classification utilizing geological images of the tunnel face.

Two-dimensional (2D) bismuthene is predicted to possess intriguing physical properties, but its preparation remains challenging due to the high surface energy constraint. Herein, we report a sandwiched epitaxy growth strategy for the controllable preparation of 2D bismuthene between a Cu foil substrate and a h-BN covering layer. The top h-BN layer plays a crucial role in suppressing the structural transformation of bismuthene and compensating for the charge transfer from the bismuthene to the Cu(111) surface. The bismuthene nanoflakes present a superior thermal stability up to 500 °C in air, attributed to the passivation effect of the h-BN layer. Moreover, the bismuthene nanoflakes demonstrate an ultrahigh faradaic efficiency of 96.3% for formate production in the electrochemical CO2reduction reaction, which is among the highest reported for Bi-based electrocatalysts. This study offers a promising approach to simultaneously synthesize and protect 2D bismuthene nanoflakes, which can be extended to other 2D materials with a high surface energy.