Toward autonomous vehicles: A survey on cooperative vehicle-infrastructure system.

Cooperative vehicle-infrastructure system (CVIS) is an important part of the intelligent transport system (ITS). Autonomous vehicles have the potential to improve safety, efficiency, and energy saving through CVIS. Although a few CVIS studies have been conducted in the transportation field recently, a comprehensive analysis of CVIS is necessary, especially about how CVIS is applied in autonomous vehicles. In this paper, we overview the relevant architectures and components of CVIS. After that, state-of-the-art research and applications of CVIS in autonomous vehicles are reviewed from the perspective of improving vehicle safety, efficiency, and energy saving, including scenarios such as straight road segments, intersections, ramps, etc. In addition, the datasets and simulators used in CVIS-related studies are summarized. Finally, challenges and future directions are discussed to promote the development of CVIS and provide inspiration and reference for researchers in the field of ITS.

Single-Molecule Discrimination of Saccharides Using Carbon Nitride Nanopores.

Structural complexity brings a huge challenge to the analysis of sugar chains. As a single-molecule sensor, nanopores have the potential to provide fingerprint information on saccharides. Traditionally, direct single-molecule saccharide detection with nanopores is hampered by their small size and weak affinity. Here, a carbon nitride nanopore device is developed to discern two types of trisaccharide molecules (LeApN and SLeCpN) with minor structural differences. The resolution of LeApN and SLeCpN in the mixture reaches 0.98, which has never been achieved in solid-state nanopores so far. Monosaccharide (GlcNAcpN) and disaccharide (LacNAcpN) can also be discriminated using this system, indicating that the versatile carbon nitride nanopores possess a monosaccharide-level resolution. This study demonstrates that the carbon nitride nanopores have the potential for conducting structure analysis on single-molecule saccharides.

Recent progress on charge transfer engineering in reticular framework for efficient electrochemiluminescence.

Electrochemiluminescence (ECL) is a luminescence production technique triggered by electrochemistry, which has emerged as a powerful analytical technique in bioanalysis and clinical diagnosis. During ECL, charge transfer (CT) is an important process between electrochemical excitation and luminescent emission, and dramatically affects the efficiency of exciton generation, playing a pivotal role in the light-emitting properties of nanomaterials. Reticular framework materials with intramolecular/intermolecular interactions offer a promising platform for regulating CT pathways and enhancing luminescence efficiency. Deciphering the role of intramolecular/intermolecular CT processes in reticular framework materials allows for the targeted design and synthesis of emitters with precisely controlled CT properties. This sheds light on the microscopic mechanisms of electro-optical conversion in ECL, propelling advancements in their efficiency and breakthrough applications. This mini-review focuses on recent advancements in engineering CT within reticular frameworks to boost ECL efficiency. We summarized strategies including intra-reticular charge transfer, CT between the metal and ligands, and CT between guest molecules and frameworks within reticular frameworks, which holds promise for developing next-generation ECL devices with enhanced sensitivity and light emission.

Timescale correlation of shallow trap states increases electrochemiluminescence efficiency in carbon nitrides.

Highly efficient interconversion of different types of energy plays a crucial role in both science and technology. Among them, electrochemiluminescence, an emission of light excited by electrochemical reactions, has drawn attention as a powerful tool for bioassays. Nonetheless, the large differences in timescale among diverse charge-transfer pathways from picoseconds to seconds significantly limit the electrochemiluminescence efficiency and hamper their broad applications. Here, we report a timescale coordination strategy to improve the electrochemiluminescence efficiency of carbon nitrides by engineering shallow electron trap states via Au-N bond functionalization. Quantitative electrochemiluminescence kinetics measurements and theoretic calculations jointly disclose that Au-N bonds endow shallow electron trap states, which coordinate the timescale of the fast electron transfer in the bulk emitter and the slow redox reaction of co-reagent at diffusion layers. The shallow electron trap states ultimately accelerate the rate and kinetics of emissive electron-hole recombination, setting a new cathodic electrochemiluminescence efficiency record of carbon nitrides, and empowering a visual electrochemiluminescence sensor for nitrite ion, a typical environmental contaminant, with superior detection range and limit.

A synthetic digital city dataset for robustness and generalisation of depth estimation models.

Existing monocular depth estimation driving datasets are limited in the number of images and the diversity of driving conditions. The images of datasets are commonly in a low resolution and the depth maps are sparse. To overcome these limitations, we produce a Synthetic Digital City Dataset (SDCD) which was collected under 6 different weather driving conditions, and 6 common adverse perturbations caused by the data transmission. SDCD provides a total of 930 K high-resolution RGB images and corresponding perfect observed depth maps. The evaluation shows that depth estimation models which are trained on SDCD provide a clearer, smoother, and more precise long-range depth estimation compared to those trained on one of the best-known driving datasets KITTI. Moreover, we provide a benchmark to investigate the performance of depth estimation models in different adverse driving conditions. Instead of collecting data from the real world, we generate the SDCD under severe driving conditions with perfect observed data in the digital world, enhancing depth estimation for autonomous driving.

Progress and summary of reinforcement learning on energy management of MPS-EV.

The escalating environmental concerns and energy crisis caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) integrate various clean energy systems to enhance the powertrain efficiency. The energy management strategy (EMS) is plays a pivotal role for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement Learning (RL) has emerged as an effective methodology for EMS development, attracting continuous attention and research. However, a systematic analysis of the design elements of RL-based EMS is currently lacking. This paper addresses this gap by presenting a comprehensive analysis of current research on RL-based EMS (RL-EMS) and summarizing its design elements. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. It highlights the contributions of advanced algorithms to training effectiveness, provides a detailed analysis of perception and control schemes, classifies different reward function settings, and elucidates the roles of innovative training methods. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Potential development directions are suggested for implementing advanced artificial intelligence (AI) solutions in EMS.

Supramolecular assembly-induced electrochemiluminescence enhancement of gold nanoclusters for hemoglobin detection.

Gold nanoclusters (Au NCs) with excellent optical properties and biocompatibility have become one of the most promising electrochemiluminescence (ECL) emitters. However, the low efficiency and poor stability of Au NCs restrict their applications in ECL. Herein, by supramolecular assembly of L-arginine (Arg) and 4-hydroxy-2-mercapto-6-methylpyrimidine (MTU) on the surface of Au NCs, Arg/MTU-Au NCs with enhanced ECL efficiency and stability were prepared. Compared with the MTU-stabilized Au NCs (MTU-Au NCs), the ECL efficiency of Arg/MTU-Au NCs increased by 24.8 times. As a proof-of-concept, a sensitive biosensing platform was constructed for sensitive detection of hemoglobin (Hb) in urine using Arg/MTU-Au NCs as ECL emitters. The proposed ECL detection platform provides a feasible strategy for the detection of biomarkers in urine and has broad application prospects in disease screening and clinical marker detection.

Probing Oxidation Mechanisms in Plasmonic Catalysis: Unraveling the Role of Reactive Oxygen Species.

Plasmon-induced oxidation has conventionally been attributed to the transfer of plasmonic hot holes. However, this theoretical framework encounters challenges in elucidating the latest experimental findings, such as enhanced catalytic efficiency under uncoupled irradiation conditions and superior oxidizability of silver nanoparticles. Herein, we employ liquid surface-enhanced Raman spectroscopy (SERS) as a real-time and in situ tool to explore the oxidation mechanisms in plasmonic catalysis, taking the decarboxylation of p-mercaptobenzoic acid (PMBA) as a case study. Our findings suggest that the plasmon-induced oxidation is driven by reactive oxygen species (ROS) rather than hot holes, holding true for both the Au and Ag nanoparticles. Subsequent investigations suggest that plasmon-induced ROS may arise from hot carriers or energy transfer mechanisms, exhibiting selectivity under different experimental conditions. The observations were substantiated by investigating the cleavage of the carbon-boron bonds. Furthermore, the underlying mechanisms were clarified by energy level theories, advancing our understanding of plasmonic catalysis.

Identifying tumor cell-released extracellular vesicles as biomarkers for breast cancer diagnosis by a three-dimensional hydrogel-based electrochemical immunosensor.

Tumor cell-released LC3+ extracellular vesicles (LC3+ EVs) participate in immunosuppression during autophagy and contribute to the occurrence and development of breast cancer. In view of the strong association between the LC3+ EVs and breast cancer, developing an effective strategy for the quantitative detection of LC3+ EVs levels with high sensitivity to identify LC3+ EVs as new biomarkers for accurate diagnosis of breast cancer is crucial, but yet not been reported. Herein, an ultrasensitive electrochemical immunosensor is presented for the quantitative determination of LC3+ EVs using a three-dimensional graphene oxide hydrogel-methylene blue composite as a redox probe, showing a low detection limit and a wide linear range. With this immunosensor, the expression levels of LC3+ EVs in various practical sample groups including different cancer cell lines, the peripheral blood of tumor-bearing mice before and after immunotherapy, and the peripheral blood from breast cancer patients with different subtypes and stages were clearly distinguished. This study demonstrated that LC3+ EVs were superior as biomarkers for the accurate diagnosis of breast cancer compared to traditional biomarkers, particularly for cancer subtype discrimination. This work would provide a new noninvasive detection tool for the early diagnosis and prognosis assessment of breast cancer in clinics.

Decoupling of the Confused Complex in Oxidation of 3,3',5,5'-Tetramethylbenzidine for the Reliable Chromogenic Bioassay.

Regulation of the reaction pathways is a perennial theme in the field of chemistry. As a typical chromogenic substrate, 3,3',5,5'-tetramethylbenzidine (TMB) generally undertakes one-electron oxidation, but the product (TMBox1) is essentially a confused complex and is unstable, which significantly hampers the clinic chromogenic bioassays for more than 50 years. Herein, we report that sodium dodecyl sulfate (SDS)-based micelles could drive the direct two-electron oxidation of TMB to the final stable TMBox2. Rather than activation of H2O2 oxidant in the one-electron TMB oxidation by common natural peroxidase, activation of the TMB substrate by SDS micelles decoupled the thermodynamically favorable complex between TMBox2 with unreacted TMB, leading to an unusual direct two-electron oxidation pathway. Mechanism studies demonstrated that the complementary spatial and electrostatic isolation effects, caused by the confined hydrophobic cavities and negatively charged outer surfaces of SDS micelles, were crucial. Further cascading with glucose oxidase, as a proof-of-concept application, allowed glucose to be more reliably measured, even in a broader range of concentrations without any conventional strong acid termination.

Ligand-induced Assembly of Copper Nanoclusters with Enhanced Electrochemical Excitation and Radiative Transition for Electrochemiluminescence.

Copper nanoclusters (CuNCs) are emerging electrochemiluminescence (ECL) emitters with unique molecule-like electronic structures, high abundance, and low cost. However, the synthesis of CuNCs with high ECL efficiency and stability in a scalable manner remains challenging. Here, we report a facile gram-scale approach for preparing self-assembled CuNCs (CuNCsAssy ) induced by ligands with exceptionally boosted anodic ECL and stability. Compared to the disordered aggregates that are inactive in ECL, the CuNCsAssy shows a record anodic ECL efficiency for CuNCs (10 %, wavelength-corrected, relative to Ru(bpy)3 Cl2 /tripropylamine). Mechanism studies revealed the unusual dual functions of ligands in simultaneously facilitating electrochemical excitation and radiative transition. Moreover, the assembly addressed the limitation of poor stability of conventional CuNCs. As a proof of concept, an ECL biosensor for alkaline phosphatase detection was successfully constructed with an ultralow limit of detection of 8.1×10-6  U/L.

Potential auto-driving threat: Universal rain-removal attack.

Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.

Carbon Nitride-Based Heterojunction Photoelectrodes with Modulable Charge-Transfer Pathways toward Selective Biosensing.

Photoelectrochemical (PEC) sensing enables the rapid, accurate, and highly sensitive detection of biologically important chemicals. However, achieving high selectivity without external biological elements remains a challenge because the PEC reactions inherently have poor selectivity. Herein, we report a strategy to address this problem by regulating the charge-transfer pathways using polymeric carbon nitride (pCN)-based heterojunction photoelectrodes. Interestingly, because of redox reactions at different semiconductor/electrolyte interfaces with specific charge-transfer pathways, each analyte demonstrated a unique combination of photocurrent-change polarity. Based on this principle, a pCN-based PEC sensor for the highly selective sensing of ascorbic acid in serum against typical interferences, such as dopamine, glutathione, epinephrine, and citric acid was successfully developed. This study sheds light on a general PEC sensing strategy with high selectivity without biorecognition units by engineering charge-transfer pathways in heterojunctions on photoelectrodes.

Outstanding feasibility of spleen stiffness measurement by 100-Hz vibration-controlled transient elastography.

This novel spleen-dedicated FibroScan has high success rate and is easy to operate. The spleen stiffness is correlated with liver stiffness, which reflects the liver fibrosis stage. However, whether SSM is able to reflect the severity of liver disease warrants further observation.

Cascade nanozymatic network mimicking cells with selective and linear perception of H2O2.

A single stimulus leading to multiple responses is an essential function of many biological networks, which enable complex life activities. However, it is challenging to duplicate a similar chemical reaction network (CRN) using non-living chemicals, aiming at the disclosure of the origin of life. Herein, we report a nanozyme-based CRN with feedback and feedforward functions for the first time. It demonstrates multiple responses at different modes and intensities upon a single H2O2 stimulus. In the two-electron cascade oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), the endogenous product H2O2 competitively inhibited substrates in the first one-electron oxidation reaction on a single-atom nanozyme (Co-N-CNTs) and strikingly accelerated the second one-electron oxidation reaction under a micellar nanozyme. As a proof-of-concept, we further confined the nanozymatic network to a microfluidic chip as a simplified artificial cell. It exhibited remarkable selectivity and linearity in the perception of H2O2 stimulus against more than 20 interferences in a wide range of concentrations (0.01-100 mM) and offered an instructive platform for studying primordial life-like processes.

Introduction to new horizons in materials for energy conversion, optics and electronics.

In conjunction with the Emerging Investigator Forum celebrating the 120th anniversary of Southeast University, we herein present a collection of articles focused on the energy conversion, optics, and electronics applications of (nano)materials.

Adaptable graphitic C6N6-based copper single-atom catalyst for intelligent biosensing.

Self-adaptability is highly envisioned for artificial devices such as robots with chemical noses. For this goal, seeking catalysts with multiple and modulable reaction pathways is promising but generally hampered by inconsistent reaction conditions and negative internal interferences. Herein, we report an adaptable graphitic C6N6-based copper single-atom catalyst. It drives the basic oxidation of peroxidase substrates by a bound copper-oxo pathway, and undertakes a second gain reaction triggered by light via a free hydroxyl radical pathway. Such multiformity of reactive oxygen-related intermediates for the same oxidation reaction makes the reaction conditions capable to be the same. Moreover, the unique topological structure of CuSAC6N6 along with the specialized donor-π-acceptor linker promotes intramolecular charge separation and migration, thus inhibiting negative interferences of the above two reaction pathways. As a result, a sound basic activity and a superb gain of up to 3.6 times under household lights are observed, superior to that of the controls, including peroxidase-like catalysts, photocatalysts, or their mixtures. CuSAC6N6 is further applied to a glucose biosensor, which can intelligently switch sensitivity and linear detection range in vitro.

Cascaded, Feedback-Driven, and Spatially Localized Emergence of Constitutional Dynamic Networks Driven by Enzyme-Free Catalytic DNA Circuits.

The enzyme-free catalytic hairpin assembly (CHA) process is introduced as a functional reaction module for guided, high-throughput, emergence, and evolution of constitutional dynamic networks, CDNs, from a set of nucleic acids. The process is applied to assemble networks of variable complexities, functionalities, and spatial confinement, and the systems provide possible mechanistic pathways for the evolution of dynamic networks under prebiotic conditions. Subjecting a set of four or six structurally engineered hairpins to a promoter P1 leads to the CHA-guided emergence of a [2 × 2] CDN or the evolution of a [3 × 3] CDN, respectively. Reacting of a set of branched three-arm DNA-hairpin-functionalized junctions to the promoter strand activates the CHA-induced emergence of a three-dimensional (3D) CDN framework emulating native gene regulatory networks. In addition, activation of a two-layer CHA cascade circuit or a cross-catalytic CHA circuit and cascaded driving feedback-driven evolution of CDNs are demonstrated. Also, subjecting a four-hairpin-modified DNA tetrahedron nanostructure to an auxiliary promoter strand simulates the evolution of a dynamically equilibrated DNA tetrahedron-based CDN that undergoes secondary fueled dynamic reconfiguration. Finally, the effective permeation of DNA tetrahedron structures into cells is utilized to integrate the four-hairpin-functionalized tetrahedron reaction module into cells. The spatially localized miRNA-triggered CHA evolution and reconfiguration of CDNs allowed the logic-gated imaging of intracellular RNAs. Beyond the bioanalytical applications of the systems, the study introduces possible mechanistic pathways for the evolution of functional networks under prebiotic conditions.

Insight into Iron Leaching from an Ascorbate-Oxidase-like Fe-N-C Nanozyme and Oxygen Reduction Selectivity.

Ascorbate (H2 A) is a well-known antioxidant to protect cellular components from free radical damage and has also emerged as a pro-oxidant in cancer therapies. However, such "contradictory" mechanisms underlying H2 A oxidation are not well understood. Herein, we report Fe leaching during catalytic H2 A oxidation using an Fe-N-C nanozyme as a ferritin mimic and its influence on the selectivity of the oxygen reduction reaction (ORR). Owing to the heterogeneity, the Fe-Nx sites in Fe-N-C primarily catalyzed H2 A oxidation and 4 e- ORR via an iron-oxo intermediate. Nonetheless, trace O2 ⋅- produced by marginal N-C sites through 2 e- ORR accumulated and attacked Fe-Nx sites, leading to the linear leakage of unstable Fe ions up to 420 ppb when the H2 A concentration increased to 2 mM. As a result, a substantial fraction (ca. 40 %) of the N-C sites on Fe-N-C were activated, and a new 2+2 e- ORR path was finally enabled, along with Fenton-type H2 A oxidation. Consequently, after Fe ions diffused into the bulk solution, the ORR at the N-C sites stopped at H2 O2 production, which was the origin of the pro-oxidant effect of H2 A.

Molecular assembly of carbon nitride-based composite membranes for photocatalytic sterilization and wound healing.

Polymeric carbon nitride (pCN) has attracted increasing interest as a metal-free photocatalyst because of its high efficiency in reactive oxygen species (ROS) generation. However, due to poor solubility, compounding pCN at the molecular level into more advanced nanocomposites remains a challenge. Herein, we report the dissolution of pCN in polyphosphoric acid (PPA) for the first time and fluid-phase assembly with carbon nanotubes (CNTs) into a flexible free-standing membrane. Mechanism and generality studies disclosed that the coordination of the acidity, viscosity, and adsorption energy of the solvents led to the successful dissolution of pCN. Interestingly, the pCN/CNTs molecular composite membrane exhibited not only superior mechanical properties and cycling performance as a result of strengthened π-π interfacial interaction, but also outstanding inactivation of E. coli and S. aureus in sterilization and wound healing for laboratory mice via photogenerated oxygen radicals. It would open a new era of pCN for biomedical applications in molecular composite membranes, beyond the traditional solar fuel applications in powders.