Hierarchically porous carbon foam composites with highly dispersed Fe2O3 nanoparticles confined in the foam pores, facilely fabricated by hydrolysis-driven emulsion polymerization strategy. The as-generated acidic conditions of Fe3+ hydrolysis could catalyze the polymerization of phenolic resin, and the carbon-based composite materials containing iron oxides were obtained in situ. The structural characterization results show that HCF@Fe2O3 NPs-2 electrode has the largest specific surface area (549 m2/ g) and pore volume (0.46 cm3/ g). Electrochemical results indicates that typical HCF@Fe2O3 NPs-2 electrode displays good capacitive properties. including high specific capacitance (225 F/g at 0.2 A/g current density). Excellent magnification performance (capacity retention rate 80% as current density increases from 0.2 to 10 A/g). At the same time, HCF@SnO2 NPs was successfully synthesized by replacing hydrolyzed tin tetrachloride with ferric chloride. This study provides a new idea for the preparation of metal oxide–carbon matrix composites, and also highlights the potential of such carbon foams in application of energy storage.

Mg81Ni19-8wt.% REO (oxides of Lanthanum and Cerium) alloys were successfully prepared using mechanical alloying method with Mg-Ni alloy and REO powder. Phase analysis, structural characterization, and microstructure imagine of the alloys were conducted using X-ray diffraction (XRD), metallurgical microscope, and transmission electron microscopy (TEM) methods. Multi-phase structures, including the primary phase of Mg2Ni and several secondary phases of Mg + Mg2Ni, MgNi-LaO, and MgNi-CeO, were found in in the as-cast Mg81Ni19- 8wt.% REO alloys. XRD and TEM results showed that Ce exhibits variable valence behavior at various stages, and the addition of REO promotes the nanocrystalline of the alloy. The hydrogen absorption capacity of ball-milled Mg81Ni19 and Mg81Ni19- 8wt.%REO alloy for 2 h at 343 K is 1.34 wt.% and 1.83 wt.%, which are much larger than 0.94 wt.% of as-cast Mg81Ni19 alloy. The addition of REO led to a decrease of the thermal decomposition temperature of the alloy hydride by approximately 20 K and a reduction of the activation energy of the hydrogen desorption reaction by 10% and 13%, respectively.

A glassy carbon electrode modified with a composite consisting of electrodeposited chitosan and carboxylated multi-walled carbon nanotubes (e-CS/MWCNTs/GCE) was used as a working electrode for simultaneous determination of dopamine (DA), serotonin (5-HT) and melatonin (MT), which were related to circadian rhythms. The electrochemical characterizations of the working electrode were carried out via electrochemical impedance spectroscopy and chronocoulometry. It was found that electrochemical modification method, that was cyclic voltammetry, may can cause continuous CS polymerization on MWCNTs surface to form a dense membrane with more active sites on the electrode, and the electrochemically active surface area of e-CS/MWCNTs/GCE obtained was about 7 times that of GCE. The electrochemical behaviour of DA, 5-HT and MT on working electrode were carried out via differential pulse voltammetry and cyclic voltammetry. The results showed that e-CS/MWCNTs/GCE solved the problem that the bare electrode could not detect three substances simultaneously, and can catalyze oxidation potential difference as low as 0.17 V of two substances reaction at the same time, indicating very good electrocatalytic activity. By optimizing the detection conditions, the sensor showed a good linear response to DA, 5-HT and MT in the range of 20-1000 μmol/L, 9-1000 μmol/L and 20-1000 μmol/L, and the detection limits were 12 μmol/L, 10 μmol/L and 22 μmol/L (S/N = 3), respectively. In addition, the proposed sensor was successfully applied to the simultaneous detection of DA, 5-HT and MT in human saliva samples.

Exploring cheap and efficient oxygen evolution reaction (OER) catalysts is extremely vital for the commercial application of advanced energy storage and conversion systems. Herein, a self-supporting Co3S4/ S-doped reduced graphene oxide ( Co3S4/S-rGO) film catalyst is successfully prepared by a blade coating coupled with high-temperature annealing strategy, and its morphology, structure and composition are measured and analyzed. It is substantiated that the as-synthesized Co3S4/ S-rGO film possesses unique self-supporting structure, and is composed of uniformly dispersed Co3S4 nanoparticles and highly conductive S-rGO, which benefit the exposure of catalytic sites and electron transfer. By reason of the synergistic effect of the two individual components, the self-supporting Co3S4/ S-rGO film catalyst displays outstanding catalytic performance towards OER. As a consequence, the Co3S4/ S-rGO film catalyst delivers an overpotential of 341 mV at 10 mA cm-2, and the current attenuation rate is only 2.6% after continuous operation for 4 h, verifying excellent catalytic activity and durability. Clearly, our results offers a good example for the construction of high-performance self-supporting carbon-based composite film catalysts for critical electrocatalytic reactions.

An environmentally friendly and low-cost chitosan-containing polysaccharide (CP) composite ZIF-8/CP was designed and prepared based on the difficulty of separating the traditional adsorbent from the water phase. ZIF-8/CP was synthesized through in-situ growth approach. The physical, chemical and structure properties of ZIF-8/CP were determined through a series of characterization methods, including SEM, FT-IR and PXRD. The effects of touch time, pH, temperature, and coexisting ions on adsorption were assessed. In addition, kinetics, isotherms of adsorption and thermodynamics were examined. The data of isotherms for adsorption indicated that the adsorption of ZIF-8/CP on MG was similar to the Langmuir model, with a maximum adsorption capacity of 1428.57 mg/g. Moreover, the kinetic parameters were consistent with the pseudo- 2nd-order equation. Thermodynamic studies (ΔG < 0, ΔH > 0) demonstrated a heat-absorbing and spontaneous adsorption process. Our study reveals that ZIF-8/CP has good adsorption properties and environmental properties.

High-quality diamond films have attracted extensive attentions due to their excellent optical and electrical properties. However, several issues, such as random orientation, stress accumulation, and slow growth rate, severely limit its applications. In this paper, high-quality polycrystalline diamond films with highly ordered (100) orientation were prepared by microwave plasma chemical vapor deposition. The effects of growth parameters on the microstructure, quality and residual stress of diamond films were investigated. Experimental results indicate that relatively high temperature at low methane concentration will promote the formation of (100) oriented grains with a low compressive stress. Optimized growth parameters, a methane concentration of 2% along with a pressure of 250 Torr and temperature at 1050 ℃, were used to acquire high growth rate of 7.9 μm/h and narrow full width at half maximum of Raman peak of 5.5 cm− 1 revealing a high crystal quality. It demonstrates a promising method for rapid growth of high-quality polycrystalline diamond films with (100) orientation, which is vital for improving the diamond related applications at low cost.

Truck no-show behavior has posed significant disruptions to the planning and execution of port operations. By delving into the key factors that contribute to truck appointment no-shows and proactively predicting such behavior, it becomes possible to make preemptive adjustments to port operation plans, thereby enhancing overall operational efficiency. Considering the data imbalance and the impact of accuracy for each decision tree on the performance of the random forest model, a model based on the Borderline Synthetic Minority Over-Sampling Technique and Weighted Random Forest (BSMOTE-WRF) is proposed to predict truck appointment no-shows and explore the relationship between truck appointment no-shows and factors such as weather conditions, appointment time slot, the number of truck appointments, and traffic conditions. In order to illustrate the effectiveness of the proposed model, the experiments were conducted with the available dataset from the Tianjin Port Second Container Terminal. It is demonstrated that the prediction accuracy of BSMOTE-WRF model is improved by 4%-5% compared with logistic regression, random forest, and support vector machines. Importance ranking of factors affecting truck no-show indicate that (1) The number of truck appointments during specific time slots have the highest impact on truck no-show behavior, and the congestion coefficient has the secondhighest impact on truck no-show behavior and its influence is also significant; (2) Compared to the number of truck appointments and congestion coefficient, the impact of severe weather on truck no-show behavior is relatively low, but it still has some influence; (3) Although the impact of appointment time slots is lower than other influencing factors, the influence of specific time slots on truck no-show behavior should not be overlooked. The BSMOTE-WRF model effectively analyzes the influencing factors and predicts truck no-show behavior in appointment-based systems.

Single-atom Pd clusters anchored on t-BaTiO3 material was synthesized using hydrothermal and ultrasonic methods for the effective piezoelectric catalytic degradation of pollutants using vibration energy. XRD patterns of BaTiO3 loaded with monoatomic Pd were obtained before and after calcining, and showed typical cubic-phase BTO. TEM and HAADF-STEM images indicated single-atom Pd clusters were successfully introduced into the BaTiO3. The piezoelectric current density of the prepared Pd-BaTiO3 binary composite was significantly higher than that of the pristine BaTiO3. Under mechanical vibration, the nanomaterial exhibited a tetracycline decomposition rate of ~95 % within 7 h, which is much higher than the degradation rate of 56.7 % observed with pure BaTiO3. Many of the piezo-induced electrons escaped to the Pd-doped BaTiO3 interface because of Pd’s excellent conductivity. Single-atom Pd clusters help promote the separation of the piezo-induced electrons, thereby achieving synergistic catalysis. This work demonstrates the feasibility of combining ultrasonic technology with the piezoelectric effect and provides a promising strategy for the development of ultrasonic and piezoelectric materials.

Aurantii Fructus Immature (AFI) and Aurantii Fructus (AF) are two important traditional Chinese herbs. As the harvesting time varies, the medicinal value of the plants is not uniform. Consequently, it has been difficult to quickly recognize them within the realm of traditional Chinese medicine. Separation and detection technologies are employed in combination to create fingerprints for identification. We proposed the utilization of graphene-assisted electrochemical fingerprint technology to acquire fingerprints of two varieties of medicinal materials. Simultaneously, we also obtained their fingerprints through HPLC. Two fingerprint recognition technologies were compared for their effectiveness. The findings demonstrate that the signals obtained through electrochemical fingerprinting have a higher recognition rate.

The flaw of low dispersibility in the metal matrix brought on by graphene's full crystal structure can be improved by the application of ion beam radiation to the surface of the material. Copper atoms are uniformly dispersed on the modified graphene oxide ( GOM) surface after being irradiated to a copper ion beam, and during the sputtering modification, the valence state of copper is changed, resulting in the formation of a new CuO phase on the graphene oxide (GO) surface. Therefore, after copper ion beam irradiation of graphene, the interfacial adhesion between GOM and copper matrix is enhanced, and the wear resistance is significantly improved. When the GOM content is low, it can withstand most of the load during the friction and wear test, which reduces the wear of the copper matrix and the occurrence of fatigue cracks at the interface of the composite material.

The emergence of Mo2C- based catalysts in recent years has been favored as promising contender within diverse class MXenes. In terms of rapid development in the photocatalytic application, these intriguing compounds exhibit excellent photocatalytic performance because of their superior optical properties and peculiar structure characteristics. Unfortunately, a systematic review of Mo2C- based catalysts is lacking. In this review, we abstract the implication of structure—property relationship of emerging Mo2C- based MXenes materials and their applications toward the photocatalytic hydrogen evolution reaction (HER). Furthermore, synthetic pathways to prepare high-quality, low cost Mo2C- based MXenes materials and their outcomes for high HER applications are systematically described. Finally, several insights are provided into the prospects and future challenges for the development of highly reactive Mo2C- based MXenes materials, which present large range opportunities in this promising 2D materials for green and clean energy in environmental fields. This review provides a comprehensive scientific guide to the preparation, modification, and photocatalytic HER of MXenes-based materials.

Amorphous molybdenum sulfide ( MoSx) has been regarded as a promising hydrogen evolution reaction (HER) catalyst due to its mild preparation conditions and low-cost precursor materials. In this work, we report a simple strategy combining the growth of amorphous MoSx on the surface of metal organic frameworks (ZIF-67) and annealing treatment to prepare Co-doped MoSx nanopolyhedrons (denoted as CoMoSx NPs). The CoMoSx NPs exhibit excellent HER activity in acid condition with an overpotential of 188 mV at a current density of 10 mA cm− 2 (η10), and a relatively stable overpotential after 2000 cyclic voltammetry (CV) cycles testing. The excellent HER performance of the CoMoSx NPs can be attributed to the doping of Co element adjust the electronic structure and increase the conductivity of catalyst, and the nanopolyhedrons structure which can expose more active sites for HER electrocatalytic. This study offers a low-cost and simple strategy to prepare high-activity HER catalyst, which holds great promises in developing advanced electrocatalysts for energy storage.

Reliable, inexpensive, environment-friendly, and durable properties of carbon materials with unique and outstanding photoelectric performance is highly desired for myriad of applications such as catalysis and energy storage. Since lattice modulation is a vital method of surface modification of materials, which form by an external force during the synthesis process, causing the internal compression and stretching, leading to lattice sliding event. In this review, we present a summary of different methods to tailor the lattice modulation in 2D carbon-based materials, including grain/twin boundary, lattice strain, lattice distortion, and lattice defects. This overview highlights the implication control of the diverse morphologies of nanocrystals and how to tailor the materials properties without adding any polymers. The improvement in the performance of 2D carbon materials ranges from the enhancement of charge transport and conductivity, structural stability, high-performance of light absorption capacity, and efficient selectivity promote the future prospect of 2D carbon materials broaden their applications in terms of energy conversion and storage. Finally, some perspectives are proposed on the future developments and challenges on 2D carbon materials towards energy storage applications.

Crowding is widely observed in various tourism contexts. Compared with natural scenic areas, theme parks have concrete boundaries and are physically, economically, or culturally separate from the surrounding area, it also brings problems with carrying capacity and visitor management. Data shows that China has become a critical theme park market, and crowding has become a major problem for theme park operators in China. This study examined the effects of human crowding on visitors' emotions, satisfaction, and behavioral intentions using the M-R model as a framework. The investigations were conducted in Shanghai Disneyland and Zhuhai Chimelong Ocean Kingdom, 295 and 410 valid questionnaires were collected respectively.

With the evolution of Artificial intelligence (AI), emotional artificial intelligence service agents (AISA) have become common in service industry. However, how artificial empathy of AISA contributes to customer acceptance remains an open question. This study draws on Anthropomorphism Theory and Customer AI Experience Theory to examine whether and how artificial empathy has influence on customer acceptance of AISA. Evidence from three experiments (N=1057) designed by the Experimental vignette method (EVM) shows that: (1) artificial empathy including perspective-taking, empathic concern and emotional contagion has a positive impact on customer acceptance of AISA (study 1); (2) customer AI experience (emotional experience quality, social experience activation and social experience quality) mediates the relationship between artificial empathy and customer acceptance of AISA (study 2); (3) artificial empathy for hedonic (vs. utilitarian) services leads to a stronger effect on customer acceptance of AISA (study 3). This paper enriches our understanding of artificial empathy and provides practical guidance for practitioners strategically managing AISA services in AI-enabled marketing interactions.

In recent years, advances in the digital and live streaming economy have led to exponential growth in the number of self-employed streamers who have become an integral part of the self-driven digital labor force. However, previous research on the impact of streamers' work time arrangements on their virtual gifts remains scarce. To fill this gap in the literature, using large-scale data from Kuaishou live streaming platform, we demonstrate that several features of streamers' work time have an important impact on their virtual gifts. Specifically, our results suggest that work time duration and timing improve streamers' virtual gifts; meanwhile, work time tempo has an adverse effect on streamers' earnings. Taken together, our results provide novel and actionable insights for millions of self-employed streamers, agencies, platforms, and policymakers.

Artificial intelligence generated content (AIGC) refers to content produced by artificial intelligence that represents the perspectives of its users, and a new technique of content Generation. Continuous development in deep learning and algorithms have facilitated the adoption of AIGC. This research summarizes literature published under the topic of AIGC using bibliometric analysis method, aims to provide insightful research directions for future studies. 342 documents were collected from Database of Web of science, network visualization analysis among authors and citation analysis over publications are presented to scholars who wish to further research into this area.

The popularity of live streaming is driving the emergence of a new business model, known as live-streaming commerce (LSC). While there are more and more broadcasters in LSC, their behaviors and performance of them are significantly different. To have a better understanding of broadcasters, we employ different machine learning models to identify different portraits in both static and dynamic dimensions. We collect a rich live-streaming dataset from one leading platform in China. Our dataset features information for both broadcasters and viewers, including viewers’ purchasing behaviors, viewers’ records of posting words, broadcasters’ gender, the number of followers for broadcasters, and the live streaming show information, including the start and end time, and the viewers in each live streaming show. The rich textual information in broadcasters’ profile induction provides us a good opportunity to uncover different static portraits and the records in live streaming shows give us a chance to identify different dynamic behavioral portraits for broadcasters.

The popularity of live streaming is driving the emergence of a new business model, known as live-streaming commerce (LSC). Consumers spend more and more time on smartphones, and the emerging business model of live-streaming commerce (LSC) is flourishing in the retail industry. With highly interactive features, LSC social interactions influence viewer purchase behaviors. To examine the interactions between influencers and viewers, we collected a rich dataset from a leading LSC service platform and integrated research models from the natural language process (NLP) field and econometric models.

The development and application of NFTs has gained great attentions. Especially, with the eyes on the potential of Metaverse and Web 3.0, NFTs are regarded as one of the foundational parts of the future internet. The main contribution of NFTs is the innovative solution for creating digital uniqueness through its property of non-fungibility. With this property, the ubiquity caused by replicable data on the current internet can be advanced with NFT-backed uniqueness, which can assist in certifying authenticity, authorship, and possessions of contents, products, and assets online. This has tremendous meaning for the luxury brand industry, which has been struggling with the ubiquity of the internet for years. The emergence of NFTs, however, represents hope and a potential mean to represent scarcity in a digital context. By the use of NFTs, luxury brands’ conservative digital marketing strategies and their ways of production design, marketing, consumer management could be fundamentally changed. This study aims to discuss the NFT marketing strategy from the perspective of luxury brands. Particularly, the study will investigate the desirability strategies in these luxury NFT cases. To do so, the study uses a socio-technical perspective to understand how luxury brands embody the desirability strategy through NFTs, by considering the technical factors of NFTs (i.e., design, issuance, and ecosystem functioning) and social factors of desirability (i.e., exclusivity, rarity, prestige, and creative leadership). The study explores applicable strategy of how to realize luxury desirability through NFT technics. As a result, this study investigates 39 luxury NFT cases from 2021 to 2022, including the NFTs launched by famous luxury brands such as Gucci, Louis Vuitton, Burberry, Dolce & Gabbana, and KARL LAGERFELD. The study showcases three within-case analyses to exhibit vivid examples of NFT innovations. Besides, the study generates a common framework by a complete cross-case analysis. The framework contains three domains and seven dimensions to guide further luxury NFT innovations and contributes to theory development in the field of NFT marketing and branding.