Near-room-temperature water-mediated densification of bulk van der Waals materials from their nanosheets.

The conventional fabrication of bulk van der Waals (vdW) materials requires a temperature above 1,000 °C to sinter from the corresponding particulates. Here we report the near-room-temperature densification (for example, ∼45 °C for 10 min) of two-dimensional nanosheets to form strong bulk materials with a porosity of <0.1%, which are mechanically stronger than the conventionally made ones. The mechanistic study shows that the water-mediated activation of van der Waals interactions accounts for the strong and dense bulk materials. Initially, water adsorbed on two-dimensional nanosheets lubricates and promotes alignment. The subsequent extrusion closes the gaps between the aligned nanosheets and densifies them into strong bulk materials. Water extrusion also generates stresses that increase with moulding temperature, and too high a temperature causes intersheet misalignment; therefore, a near-room-temperature moulding process is favoured. This technique provides an energy-efficient alternative to design a wide range of dense bulk van der Waals materials with tailored compositions and properties.

A latent profile analysis of psychological resilience associated with work fatigue among Chinese nurses.

Nurses' psychological resilience has been studied using a variable-centered rather than a person-centered approach, impeding a comprehensive understanding of the holistic expression of these different forms of psychological resilience. This study aimed to examine the characteristics of psychological resilience in a sample of Chinese nurses during COVID-19 using latent profile analysis (LPA) and the relationships between these unique profiles and nurses' work fatigue. A total of 471 Chinese nurses were recruited from three hospitals in Hebei Province between June and July 2020. The participant samples were investigated with the psychological resilience scale and the work fatigue inventory. LPA showed that a three-profile model of psychological resilience best fit this study. The resulting profiles were low psychological resilience, medium psychological resilience, and high psychological resilience. Notably, there were significant differences in nurses' work fatigue among different profiles of psychological resilience. The study provided a new view of nurses' psychological resilience, which may be used to effectively reduce nurses' work fatigue and evaluate nursing work status comprehensively.

Latent profiles of ethical climate and nurses' service behavior.

BACKGROUND: Hospital ethical climate has important implications for clinical nurses' service behavior; however, the relationships are complicated by the fact that five types of ethical climate (caring, law and code, rules, instrumental, and independence) can be combined differently according to their level and shape differences. Recent developments in person-centered methods (e.g., latent profile analysis (LPA)) have helped to address these complexities. AIM: From a person-centered perspective, this study explored the distinct profiles of hospital ethical climate and then examined the relationships of the profiles with clinical nurses' service behavior (both in-role and extra-role service behavior). RESEARCH DESIGN: A quantitative study was conducted using cluster random sampling. Latent profile analysis and binary coded hexadecimal (BCH) analysis were conducted using Mplus 8.2. PARTICIPANTS AND RESEARCH CONTEXT: A total of 871 clinical nurses in China were surveyed using the Ethical Climate Scale and Nurses' Service Behavior Questionnaire. ETHICAL CONSIDERATIONS: Ethical approval was obtained from the IRB of the First Affiliated Hospital of Jinan University (No. KY-2020-090). RESULTS: A four-profile hospital ethical climate model provided the best fit for the data. The four different profiles not only varied in level, but also in shape: high normative and low egoism (45.8%), high ethical climate (19.9%), low ethical climate (3.6%), and moderate ethical climate (30.8%). These profiles differentially predicted clinical nurses' overall, in-role, and extra-role service behaviors. CONCLUSIONS: The results reveal new insights into the nature of hospital ethical climate and how different clinical nurses in these profiles can be best managed to accomplish different forms of service behavior.

The effect of perceived organizational support on the work engagement of Chinese nurses during the COVID-19: the mediating role of psychological safety.

The outbreak of COVID-19 undoubtedly aggravated the pressure and workload of nurses' work, which may bring new challenges to nurses' work engagement. This study aims to explore the factors of nurses' work engagement, and it may provide targeted references for clinical intervention. Convenience sampling was used, and 689 nurses from Hebei and Guangxi Province in China were investigated. The results indicated that both perceived organizational support and psychological safety have direct positive impacts on nurses' work engagement, and psychological safety mediates the relationship between perceived organizational support and work engagement. These findings provide new ideas to help nursing managers better understand how to improve work engagement.

Effects of role overload, work engagement and perceived organisational support on nurses' job performance during the COVID-19 pandemic.

AIMS: We aim to study the effect of role overload, work engagement and perceived organisational support on nurses' job performance, including task performance, interpersonal facilitation and job dedication. BACKGROUND: Many nurses have suffered from role overload at work during the COVID-19 pandemic. However, the investigations of the influence mechanisms and boundary conditions through and under which role overload is associated with job performance have shown inconsistent results. METHODS: A total of 595 Chinese nurses were studied from November 2020 to February 2021. Confirmatory factor analysis, maximum likelihood estimation and bootstrapping analysis were used to test the mediating process and the moderating effect. RESULTS: Work engagement partly mediated the relationships of role overload with task performance (ß = -.253, p < .001, 95% CI: [-.315, -.204]) and interpersonal facilitation (ß = -.202, p < .001, 95% CI: [-.261, -.145]); work engagement also fully mediated the relationship between role overload and job dedication (ß = -.239, p < .001, 95% CI: [-.302, -.186]). Perceived organisational support moderated the relationships of role overload with task performance, interpersonal facilitation and work dedication (ß = -.171, p < .001, ß = -.154, p < .001 and ß = -.175, p < .001, respectively). CONCLUSIONS: Work engagement is the linchpin linking role overload to distal outcomes of job performance. Perceived organisational support mitigates the ways in which role overload undermines job performance. IMPLICATIONS FOR NURSING MANAGEMENT: Hospital administrators can minimize the effects of role overload and create a more supportive organisational environment to promote the job performance of nurses.

Effects of ethical leadership on nurses' service behaviors.

BACKGROUND: Nurses' service behaviors have critical implications for hospitals. However, few studies had adequate ethical considerations of service behaviors and accounted for how organizational or individual antecedents can induce nurses to engage in service behaviors. In addition, they mainly focused on the one side of role-prescribed or extra-role service behavior. OBJECTIVE: This study aims to explore the chained mediation effect of ethical climate and moral sensitivity on the relationship between organizational ethical leadership and nurses' service behaviors and to examine the relationship, from a comparative view, of the role-prescribed service behavior and extra-role service behavior. METHODS: In all, 476 nurses from three tertiary hospitals were investigated with the Ethical Leadership Scale, Ethical Climate Scale, Moral Sensitivity Questionnaire and Service Behavior Questionnaire. Structural equation modeling was adopted to analyze the data. SPSS and Mplus statistical software was used in the data analysis. ETHICAL CONSIDERATIONS: Approval was obtained from the Ethics Committee at School of Nursing, Hebei Medical University. Data privacy and confidentiality were maintained and assured by obtaining subjects' informed consent to participate in the research before data collection. RESULTS: The effects of ethical leadership on nurses' service behaviors are mediated by two variables in turn: ethical climate and nurses' moral sensitivity. Ethical climate and moral sensitivity partially mediated the relationship between ethical leadership and nurses' role-prescribed service behavior and fully mediated the relationship between ethical leadership and nurses' extra-role service behavior. CONCLUSION: Organizational ethical leadership positively affected ethical climate, which positively affected nurses' moral sensitivity and affected both their role-prescribed service behavior and extra-role service behavior.

Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest.

Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.

China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression.

Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.

Highly efficient and selective extraction of gold by reduced graphene oxide.

Materials capable of extracting gold from complex sources, especially electronic waste (e-waste), are needed for gold resource sustainability and effective e-waste recycling. However, it remains challenging to achieve high extraction capacity and precise selectivity if only a trace amount of gold is present along with other metallic elements . Here we report an approach based on reduced graphene oxide (rGO) which provides an ultrahigh capacity and selective extraction of gold ions present in ppm concentrations (>1000 mg of gold per gram of rGO at 1 ppm). The excellent gold extraction performance is accounted to the graphene areas and oxidized regions of rGO. The graphene areas spontaneously reduce gold ions to metallic gold, and the oxidized regions allow good dispersibility of the rGO material so that efficient adsorption and reduction of gold ions at the graphene areas can be realized. By controlling the protonation of the oxidized regions of rGO, gold can be extracted exclusively, without contamination by the other 14 co-existing elements typically present in e-waste. These findings are further exploited to demonstrate recycling gold from real-world e-waste with good scalability and economic viability, as exemplified by using rGO membranes in a continuous flow-through process.

Investigation of Exciton Recombination Zone in Quantum Dot Light-Emitting Diodes Using a Fluorescent Probe.

Exciton recombination zone, where the photons are generated, can greatly affect the performance, such as the efficiency and color purity, of the quantum dot (QD) light-emitting diodes (QLEDs). To probe the exciton recombination zone, 4-(dicyanomethylene)-2-t-butyl-6(1,1,7,7-tetramethyljulolidyl-9-enyl)-4H-pyran (DCJTB) is doped into the charge transport layer as a fluorescent sensor; by monitoring the Förster resonant energy transfer (FRET) between QD and DCJTB, the location of the recombination zone can be determined. It is found that the electron transport layer (ETL) has a great impact on the recombination zone. For example, in QLEDs with ZnMgO ETL, the recombination zone is near the interface of the QD/hole transport layer (HTL) and is shifted to the interface of the QD/ETL as the driving voltage is increased, whereas in devices with 1,3,5-tris(2-N-phenylbenzimidazolyl) benzene (TPBi) ETL, the recombination zone is close to the interface of the QD/ETL and moved to the interface of the QD/HTL with the increase in the driving voltage. Our results can also clarify the light emission mechanism in QLEDs. In devices with ZnMgO ETL, the emission is dominated by the direct charge recombination, whereas in devices with TPBi ETL, the emission is contributed by both FRET and direct charge recombination. Our studies suggest that fluorescent probe can be a powerful tool for investigating the exciton recombination zone, light emission mechanism, and other fundamental processes in QLEDs.