Shandong University of Science and Technology

Global development has been heavily reliant on the overexploitation of natural resources since the Industrial Revolution. With the extensive use of fossil fuels, deforestation, and other forms of land-use change, anthropogenic activities have contributed to the ever-increasing concentrations of greenhouse gases (GHGs) in the atmosphere, causing global climate change. In response to the worsening global climate change, achieving carbon neutrality by 2050 is the most pressing task on the planet. To this end, it is of utmost importance and a significant challenge to reform the current production systems to reduce GHG emissions and promote the capture of CO2 from the atmosphere. Herein, we review innovative technologies that offer solutions achieving carbon (C) neutrality and sustainable development, including those for renewable energy production, food system transformation, waste valorization, C sink conservation, and C-negative manufacturing. The wealth of knowledge disseminated in this review could inspire the global community and drive the further development of innovative technologies to mitigate climate change and sustainably support human activities.

Change detection is an important task in remote sensing (RS) image analysis. It is widely used in natural disaster monitoring and assessment, land resource planning, and other fields. As a pixel-to-pixel prediction task, change detection is sensitive about the utilization of the original position information. Recent change detection methods always focus on the extraction of deep change semantic feature, but ignore the importance of shallow-layer information containing high-resolution and fine-grained features, this often leads to the uncertainty of the pixels at the edge of the changed target and the determination miss of small targets. In this letter, we propose a densely connected siamese network for change detection, namely SNUNet-CD (the combination of Siamese network and NestedUNet). SNUNet-CD alleviates the loss of localization information in the deep layers of neural network through compact information transmission between encoder and decoder, and between decoder and decoder. In addition, Ensemble Channel Attention Module (ECAM) is proposed for deep supervision. Through ECAM, the most representative features of different semantic levels can be refined and used for the final classification. Experimental results show that our method improves greatly on many evaluation criteria and has a better tradeoff between accuracy and calculation amount than other state-of-the-art (SOTA) change detection methods.

Here, we report hydrogen generation from the direct splitting of water by photocatalysis is regarded as a promising and renewable solution for the energy crisis. The key to realize this reaction is to find an efficient and robust photocatalyst that ideally makes use of the energy from sunlight. Recently, due to the attractive properties such as appropriate band structure, ultrahigh specific surface area, and more exposed active sites, two-dimensional (2D) photocatalysts have attracted significant attention for photocatalytic water splitting. This Review attempts to summarize recent progress in the fabrication and applications of 2D photocatalysts including graphene-based photocatalysts, 2D oxides, 2D chalcogenides, 2D carbon nitride, and some other emerging 2D materials for water splitting. The construction strategies and characterization techniques for 2D/2D photocatalysts are summarized. Particular attention has been paid to the role of 2D/2D interfaces in these 2D photocatalysts as the interfaces and heterojunctions are critical for facilitating charge separation and improving photocatalysis efficiency. We also critically discuss their stability as photocatalysts for water splitting. Lastly, we highlight the ongoing challenges and opportunities for the future development of 2D photocatalysts in this exciting and still emerging area of research.

Significance Electrolysis of water to generate hydrogen fuel could be vital to the future renewable energy landscape. Electrodes that can sustain seawater splitting without chloride corrosion could address the issue of freshwater scarcity on Earth. Herein, a hierarchical anode consisting of a nickel–iron hydroxide electrocatalyst layer uniformly coated on a sulfide layer formed on Ni substrate was developed, affording superior catalytic activity and corrosion resistance in seawater electrolysis. In situ-generated polyanion-rich passivating layers formed in the anode are responsible for chloride repelling and high corrosion resistance, leading to new directions for designing and fabricating highly sustained seawater-splitting electrodes and providing an opportunity to use the vast seawater on Earth as an energy carrier.

Novel red-emissive carbon-dots (C-dots) with broad absorption in the region from 400 to 750 nm are prepared from polythiophene phenylpropionic acid. Upon near infrared laser irradiation, the red-emissive C-dots show strong photoacoustic response and high photothermal conversion efficiency (η ≈ 38.5%). These unique properties enable the C-dots to act as multifunctional fluorescent, photoacoustic, and thermal theranostics for simultaneous diagnosis and therapy of cancer. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Abstract Binary NiFe layer double hydroxide (LDH) serves as a benchmark non‐noble metal electrocatalyst for the oxygen evolution reaction, however, it still needs a relatively high overpotential to achieve the threshold current density. Herein the catalyst's electronic structure is tuned by doping vanadium ions into the NiFe LDHs laminate forming ternary NiFeV LDHs to reduce the onset potential, achieving unprecedentedly efficient electrocatalysis for water oxidation. Only 1.42 V (vs reversible hydrogen electrode (RHE), ≈195 mV overpotential) is required to achieve catalytic current density of 20 mA cm −2 with a small Tafel slope of 42 mV dec −1 in 1 m KOH solution, which manifests the best of NiFe‐based catalysts reported till now. Electrochemical analysis and density functional theory +U simulation indicate that the high catalytic activity of NiFeV LDHs mainly attributes to the vanadium doping which can modify the electronic structure and narrow the bandgap thereby bring enhanced conductivity, facile electron transfer, and abundant active sites.

The infrared spectrum (IR) characteristic peaks of amide I, amide II, and amide III bands are marked as amide or peptide characteristic peaks. Through the nuclear magnetic resonance study, N-methylacetamide has been determined to have six fine components, which include protonation, hydration, and hydroxy structures. Then the independent IR spectrum of every component in N-methylacetamide is calculated by using the density functional theory quantum chemistry method, and the contribution of each component to amide I, II, and III bands is analyzed. The results of this research can help to explain the formation of the amide infrared spectrum, which has positive significance in organic chemistry, analytical chemistry, and chemical biology.

Intrinsically conducting polymers (ICP) and conductive fillers incorporated conductive polymer-based composites (CPC) greatly facilitate the research in electromagnetic interference (EMI) shielding because they not only provide excellent EMI shielding but also have advantages of electromagnetic wave absorption rather than reflection. In this review, the latest developments in ICP and CPC based EMI shielding materials are highlighted. In particular, existing methods for adjusting the morphological structure, electric and magnetic properties of EMI shielding materials are discussed along with the future opportunities and challenges in developing ICP and CPC for EMI shielding applications.

Abstract. Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R2) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m−3, a small mean absolute error of 6.69 (7.15) µg m−3 and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R2=0.80), which can be used to estimate historical PM2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM2.5 dataset across mainland China (i.e., ChinaHighPM2.5), important for air pollution studies focused on urban areas.

Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. Thus, it can capture long-range relations for computer vision tasks. However, it is computationally consuming. Since the attention maps are computed w.r.t all other positions. In this paper, we formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed. By a weighted summation upon these bases, the resulting representation is low-rank and deprecates noisy information from the input. The proposed Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Moreover, we set up the bases maintenance and normalization methods to stabilize its training procedure. We conduct extensive experiments on popular semantic segmentation benchmarks including PASCAL VOC, PASCAL Context, and COCO Stuff, on which we set new records <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

In this paper, a delayed virus model with two different transmission methods and treatments is investigated. This model is a time-delayed version of the model in (Zhang et al. in Comput. Math. Methods Med. 2015:758362, 2015). We show that the virus-free equilibrium is locally asymptotically stable if the basic reproduction number is smaller than one, and by regarding the time delay as a bifurcation parameter, the existence of local Hopf bifurcation is investigated. The results show that time delay can change the stability of the endemic equilibrium. Finally, we give some numerical simulations to illustrate the theoretical findings.

Abstract Recently, interest in aluminium ion batteries with aluminium anodes, graphite cathodes and ionic liquid electrolytes has increased; however, much remains to be done to increase the cathode capacity and to understand details of the anion–graphite intercalation mechanism. Here, an aluminium ion battery cell made using pristine natural graphite flakes achieves a specific capacity of ∼110 mAh g −1 with Coulombic efficiency ∼98%, at a current density of 99 mA g −1 (0.9 C) with clear discharge voltage plateaus (2.25–2.0 V and 1.9–1.5 V). The cell has a capacity of 60 mAh g −1 at 6 C, over 6,000 cycles with Coulombic efficiency ∼ 99%. Raman spectroscopy shows two different intercalation processes involving chloroaluminate anions at the two discharging plateaus, while C–Cl bonding on the surface, or edges of natural graphite, is found using X-ray absorption spectroscopy. Finally, theoretical calculations are employed to investigate the intercalation behaviour of choloraluminate anions in the graphite electrode.

Abstract In this study, a binary networked conductive hydrogel is prepared using acrylamide and polyvinyl alcohol. Based on the obtained hydrogel, an ultrastretchable pressure sensor with biocompatibility and transparency is fabricated cost effectively. The hydrogel exhibits impressive stretchability (&gt;500%) and superior transparency (&gt;90%). Furthermore, the self‐patterned microarchitecture on the hydrogel surface is beneficial to achieve high sensitivity (0.05 kPa −1 for 0–3.27 kPa). The hydrogel‐based pressure sensor can precisely monitor dynamic pressures (3.33, 5.02, and 6.67 kPa) with frequency‐dependent behavior. It also shows fast response (150 ms), durable stability (500 dynamic cycles), and negligible current variation (6%). Moreover, the sensor can instantly detect both tiny (phonation, airflowing, and saliva swallowing) and robust (finger and limb motions) physiological activities. This work presents insights into preparing multifunctional hydrogels for mechanosensory electronics.

Many real-world IoT systems, which include a variety of internet-connected sensory devices, produce substantial amounts of multivariate time series data. Meanwhile, vital IoT infrastructures like smart power grids and water distribution networks are frequently targeted by cyber-attacks, making anomaly detection an important study topic. Modeling such relatedness is, nevertheless, unavoidable for any efficient and effective anomaly detection system, given the intricate topological and nonlinear connections that are originally unknown among sensors. Furthermore, detecting anomalies in multivariate time series is difficult due to their temporal dependency and stochasticity. This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture. The connection learning policy, which is based on the Gumbel-softmax sampling approach to learn bi-directed links among sensors directly, is at the heart of learning graph structure. To describe the anomaly information flow between network nodes, we introduced a new graph convolution called Influence Propagation convolution. In addition, to tackle the quadratic complexity barrier, we suggested a multi-branch attention mechanism to replace the original multi-head self-attention method. Extensive experiments on four publicly available anomaly detection benchmarks further demonstrate the superiority of our approach over alternative state-of-the-arts. Codes are available at https://github.com/ZEKAICHEN/GTA.

The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent years have been dramatically incentivizing the advancement of edge computing. On the one hand, edge computing has provided a great assistance for lightweight devices to accomplish complicated tasks in an efficient way; on the other hand, its hasty development leads to the neglection of security threats to a large extent in edge computing platforms and their enabled applications. In this paper, we provide a comprehensive survey on the most influential and basic attacks as well as the corresponding defense mechanisms that have edge computing specific characteristics and can be practically applied to real-world edge computing systems. More specifically, we focus on the following four types of attacks that account for 82% of the edge computing attacks recently reported by Statista: distributed denial of service attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks. We also analyze the root causes of these attacks, present the status quo and grand challenges in edge computing security, and propose future research directions.

Abstract Until now there has been no fundamental theory applicable for biodegradable metals (BMs). First, this paper optimizes the definition of BMs given in 2014. Second, the dual criteria of biodegradability and biocompatibility are proposed for BMs, and all metallic elements in the periodic table with accessible data are screened on the basis of these criteria. Regarding biodegradability, electrode potential, reactivity series, galvanic series, Pilling–Bedworth ratio, and Pourbaix diagrams are all adopted as parameters to classify the degradable and nondegradable nature of a material, especially in a physiological environment. Considering the biocompatibility at different levels, cellular biocompatibility, tissue biocompatibility, and human/clinical related biocompatibility parameters are put forward to comprehensively evaluate the biosafety of BMs. Third, for the material design of BMs, mechanical properties, chemical properties, physical properties and biological properties should be considered and balanced to guarantee that the degradation behavior of BMs match well with a tissue regeneration/repair procedure as the function of time and spatial location. Besides the selected metallic elements, some nonmetallic elements are selected as suitable alloying elements for BMs. Finally, five classification/research directions for future BMs are proposed: biodegradable pure metals, crystalline alloys, bulk metallic glasses, high entropy alloys, and metal matrix composites.

Molecularly imprinted polymers (MIPs) have now earned the reputation as "artificial receptors" or "plastic antibodies". As the mimics of natural receptors, MIPs are reminiscent of some basic functions of natural receptors in living systems, e.g., the ability to interact with or recognize cells. The latest decade has witnessed a great advance in MIPs from simple molecular extraction to efficient cell recognition, implying that MIP-based synthetic receptors are approaching to be perfectly functioning replicates of their natural counterparts. With the most emerging development in molecular imprinting, MIP-mediated cell recognition has now shown great promise in cell biology research, theranostics and regenerative medicine. This tutorial review provides a panoramic view of current MIPs for both microorganism and mammalian cell recognition. The most representative developments of MIP-mediated cell recognition, from initial imprinting strategies to eventual bio-related applications, are highlighted.

Stereopsis provides an additional depth cue and plays an important role in the human vision system. This paper explores stereopsis for saliency analysis and presents two approaches to stereo saliency detection from stereoscopic images. The first approach computes stereo saliency based on the global disparity contrast in the input image. The second approach leverages domain knowledge in stereoscopic photography. A good stereoscopic image takes care of its disparity distribution to avoid 3D fatigue. Particularly, salient content tends to be positioned in the stereoscopic comfort zone to alleviate the vergence-accommodation conflict. Accordingly, our method computes stereo saliency of an image region based on the distance between its perceived location and the comfort zone. Moreover, we consider objects popping out from the screen salient as these objects tend to catch a viewer's attention. We build a stereo saliency analysis benchmark dataset that contains 1000 stereoscopic images with salient object masks. Our experiments on this dataset show that stereo saliency provides a useful complement to existing visual saliency analysis and our method can successfully detect salient content from images that are difficult for monocular saliency analysis methods.

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) μg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 μg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.

One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the recent State-Of-The-Art (SOTA) models for this task tends to be exceedingly sophisticated and over-parameterized. The low efficiency in model training and inference has increased the validation costs of model architectures in large-scale datasets. To address the above issue, recent advanced separable convolutional layers are embedded into an early fused Multiple Input Branches (MIB) network, constructing an efficient Graph Convolutional Network (GCN) baseline for skeleton-based action recognition. In addition, based on such the baseline, we design a compound scaling strategy to expand the model's width and depth synchronously, and eventually obtain a family of efficient GCN baselines with high accuracies and small amounts of trainable parameters, termed EfficientGCN-Bx, where "x" denotes the scaling coefficient. On two large-scale datasets, i.e., NTU RGB+D 60 and 120, the proposed EfficientGCN-B4 baseline outperforms other SOTA methods, e.g., achieving 92.1% accuracy on the cross-subject benchmark of NTU 60 dataset, while being 5.82× smaller and 5.85× faster than MS-G3D, which is one of the SOTA methods. The source code in PyTorch version and the pretrained models are available at https://github.com/yfsong0709/EfficientGCNv1.