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Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

Thiadiazole compounds and their derivatives have carrying capacity and good lubricating properties. However, their poor oil-solubility limited their wide usage in lubricating oil. The study aims to develop thiadiazole lubricant additives with better oil-solubility. When the hindered phenol antioxidation functional group and alkyl-chain are introduced to thiadiazole, the resulting product could have better oil-solubility and excellent antioxidation resistance, anti-wear and corrosion resistance in the lubricating oil.

One kind of thiadiazole lubricant additive, for the first time, has been synthesized from 2,5-dimercapto-1,3,4-thiadiazole. Its tribological performance has also been evaluated by four-ball test. And, its oxidation resistance has been estimated by rotating pressure vessel oxidation test and pressurized differential scanning calorimetry. The anticorrosion performance of such an additive has been studied by GB/T 5096 standard method test.

The synthesized thiadiazole additive has excellent anti-oxidation capability, good anti-wear and extreme pressure properties and good anticorrosion performance, in comparison with zinc dialkyl dithiophosphate (ZDDP). In base oils, the comprehensive performance of thiadiazole additive is comparable to ZDDP. Mechanistic studies indicate that the S and N active elements were involved in the formation of a boundary film. This may account for the formation of a composite membrane on the metal surface and thus for the tribological performance of such thiadiazole additives.

The thiadiazole derivatives, which are ashless and have environmentally friendly features, are a potential alternative to ZDDP. Because of the film formed on the friction surface, thiadiazole compounds may serve as an excellent anti-wear additive and are expected to reduce friction and wear between metals.

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

This study aims to investigate the effect of coatings prepared by the addition of copper-aluminum alloy powder on the corrosion behavior of 90/10 copper-nickel alloy.

Coatings of copper-aluminum alloy powder at different contents (Wt.% = 50%, 60%, 70% and 80%) were prepared by the high-temperature heat treatment process. The microstructure and component of the coatings were characterized by scanning electron microscope, X-ray diffraction, energy dispersive spectrometer and X-ray photoelectron spectroscopy. The electrochemical properties of the coating were explored by electrochemical impedance spectroscopy.

The results show that the aluminized layer was successfully constructed on the surface of 90/10 copper-nickel alloy, the composition of the coating was composed of copper-aluminum phase and aluminum-nickel phase, the existence of the aluminum-nickel phase was formed by the diffusion of Ni elements within the substrate and because of the diffusion, the Al-Ni phase was distributed in the middle and bottom of the coating more. The Al-Ni phase is considered to be the enhanced phase for corrosion resistance. When the copper-aluminum alloy powder content is 70 Wt.%, the corrosion resistance is the best.

The enhancement of corrosion resistance of 90/10 copper-nickel alloy by the copper-aluminum alloy powder was revealed, the composition of the aluminized layer and the mechanism of corrosion resistance were discussed.

This study aims to investigate the effect of changes in iron content in 70/30 copper–nickel alloy on the corrosion process.

70Copper–30Nickel-xFe-1Mn (x = 0.4,0.6,0.8,1.0 Wt.%) alloy were prepared by the high frequency induction melting furnace. The scanning electron microscope, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and electrochemical impedance spectroscopy were used to analyze the morphology and component of the corrosion product film.

The results show that the corrosion resistance of 70/30 copper–nickel alloy added with 1.0%Fe is the best, and the film is divided into inner dense Cu2O composite film and outer hydration loose layer; XRD showed that after adding 1.0% Fe, the content of Cu2(OH)3Cl in the corrosion product film was significantly reduced, while the content of Cu2O remained unchanged; XPS showed that nickel accumulates in the inner layer of corrosion product film; the stage growth mode of the film, the role of nickel in it and the enrichment mechanism of iron in the inner film were summarized and discussed.

The changes in the composition and structure of the corrosion product film caused by the iron content are revealed, and the mechanism of the difference in corrosion resistance is discussed.

This paper aims to investigate the impact of open innovation (OI) in Chinese enterprises on enterprise value (EV). At the same time, this research explores the effects of OI in environmental, social and governance (ESG) and also reveals the intermediary role of ESG in the impact of OI on improving EV.

This study builds a theoretical framework to define a set of hypotheses verified in empirical research. Based on the panel data of Chinese listed companies removing missing data from 2011 to 2020, a two-way fixed effect model is used to study the relationship between OI, ESG and EV.

The research shows that OI of enterprises has effectively improved their value. At the same time, OI can also improve the ESG of enterprises. That OI increases EV is partly by the mediating path of ESG.

This study determines the influence of OI on EV from a stakeholder and sustainable development perspective, clarifying the mediating pathway of ESG in the relationship. Doing so could inspire companies to improve their OI and desire to enhance competitiveness. It will also prompt enterprises to pay attention to social responsibility activities.

Team learning is critical to interdisciplinary research teams (IDR teams) to use heterogeneous knowledge effectively. Nevertheless, team learning is rarely addressed in the IDR team literature. Also, few studies investigate the antecedents and consequences of team learning in IDR teams, leading to a lack of guidance for management practices. This study aims to investigate how team learning can be developed and how team learning influences team outcomes in IDR teams.

A questionnaire survey on 304 members of 37 IDR teams in a research university in China is conducted. Data are analyzed using a partial least square structural equation modeling.

The results support most hypotheses in general. For the antecedent variables, task interdependence, trust and constructive conflict positively affect team learning. For the outcome variables, team learning improves shared mental models, coordination quality and team performance significantly. Additionally, task uncertainty positively moderates the team learning-coordination quality relation and team learning-team performance relation. However, this paper does not find support for the moderating role of task uncertainty on the team learning-shared mental models relation.

To the best of the knowledge, this is the first study investigating the antecedents and consequences of team learning in IDR teams. A multidimensional measurement of team learning for the IDR team context is developed. This study investigates how team behavioral factors influence team learning and the effect of team learning on shared mental models, coordination quality and team performance. This study also explores the contingency role of task uncertainty in the effects of team learning.

This paper aims to study the effect of load on the tribological behaviour of Cu-based composites, so as to obtain a suitable applied load on these composites.

Cu-based composites were prepared by powder sintering with direct current electric current heating and tested by Universal Mechanical Test-3 with a ball-on-disk at room temperature.

The results showed that Cu-based composites are might suitable for working under low load. There is only mild damage on the surface under a load of 2 N. While it has microcracks and shows signs of cavitation at a certain depth at 20 N and 50 N. In addition, it is evident that there are three zones in the cross-section of the matrix, namely, a mechanical mixing layer, ceramic layer and substrate, respectively.

There are two wear mechanisms at different loads, and the evolution of worn surfaces with sliding time is also involved. Thus, the developed material can be used for light load sliding electrical contact material applications.

The purpose of this study is to bridge the gap in the literature on supply chain finance (SCF) by exploring the relationship between network capabilities and corporate financial performance (CFP) in financial supply chains (FSCs).

The authors collect panel data and adopt regression analysis to analyse the joint investment activities among 1359 manufacturing firms and 289 financial service providers in China to explore how network capabilities, both network power and network centrality, improve CFP in the FSCs.

Under the FSCs environments, network centrality (i.e. eigenvector centrality, closeness centrality and betweenness centrality) raises CFP (ROA, ROE and Tobin's Q) and network power (node degree, clustering coefficient) also improves CFP. However, node strength from the network power stream has a negative effect on Tobin's Q, indicating that when the partner of a firm has an extremely strong influence in FSCs; this weakens the bargaining ability and flexibility of the focal firm, thus reducing its long-term financial performance.

The joint investment activities among supply chain partners and financial service providers help managers understand the advanced financing solutions generated by internal and external network organisations as well as be aware of network capabilities' impact on CFP in FSCs.

This study answers the call for more empirical research on SCF to provide a broader sample to examine financial supply chain management. This is one of the earliest studies to shed light on a new perspective – how network capabilities improve CFP in the FSCs.