Diabetic foot ulcers (DFUs) are a serious complication of diabetes and often result in amputation. Traditional wound care methods have limitations in addressing the complex pathophysiology of DFUs. Hydrogel dressings, a type of biomaterial, have emerged as promising candidates for treating DFUs due to their biocompatibility, ability to retain moisture, and potential to incorporate therapeutic agents. Hydrogels create a moist environment, promote cell migration, and reduce inflammation, thereby supporting wound healing. Incorporating bioactive molecules, such as growth factors and anti-inflammatory agents, can further enhance the effectiveness of hydrogels. Additionally, stem cells can be loaded into hydrogels to improve tissue regeneration and help modulate the wound microenvironment. Recent advancements in hydrogel technology have also led to the development of smart hydrogels that can respond to changes in wound conditions, such as glucose levels and pH. These intelligent dressings offer personalized care by delivering targeted treatments based on real-time wound data. This review explores the mechanisms behind DFU development, the role of hydrogels in wound healing, and recent progress in hydrogel technologies for personalized DFU care.

This study integrates ChatGPT and complex network (CN) techniques into an accident analysis framework designed to reduce manual effort in accident causation analysis. The proposed framework supports construction stakeholders in extracting causal factors (CFs) from accident reports and identifying both critical CFs and key causal paths. A multistep research design was adopted to develop and validate this novel framework for analyzing crane-related construction accident reports using ChatGPT and CN techniques. First, ChatGPT was prompted to extract CFs from a database of crane-related accident reports. Second, evaluation metrics and an expert questionnaire survey were developed to assess ChatGPT’s performance in CF extraction. Finally, CN analysis was conducted to explore the relationships among CFs and to identify critical causal paths. A total of 95 crane-related accidents from Hong Kong (2011-2020) were analyzed using the proposed framework. The critical CFs identified included: “carelessness”, “operation error”, “crane unbalanced”, “machine failure”, “parts of a crane fall”, “object strike”, “worker fall”, “trapping”, “collapse of crane”, and “load drop”. The critical path identified was: “broken/failed rope” → “load drop” → “object strike”. The primary contribution of this study lies in developing an AI-driven framework that combines the contextual understanding of ChatGPT with the structural analysis capabilities of CN methods—offering a novel and scalable approach to accident causation analysis in the construction industry. Safety managers and practitioners can leverage this framework to improve the automation, consistency, and interpretability of construction accident reporting.

Photocatalytic reduction of CO₂ to produce valuable fuels or chemicals is a promising CO₂ utilization technology, which is of great significance for carbon emission reduction. The unique features of the UiO series of metal-organic frameworks (MOFs), such as the excellent water and chemical stability, notable structural tunability, broad and adjustable light-harvesting capacity, strong electron-hole separation ability, and high porosity and specific surface area, make them a class of photocatalysts with great potential for the CO₂ reduction reaction (CO₂RR). Significant progress has been made in the development of efficient UiO-based photocatalysts for CO₂RR. This paper provides a summary of recent research advances in UiO-MOFs for photocatalytic CO₂RR. The characteristics, synthesis methods, and modifications of UiO-based materials, along with their photocatalytic performance, are described. Various modification strategies for UiO-MOFs, including band-gap engineering, defect engineering, introduction of metal species, and construction of composite materials, are summarized and discussed. The challenges facing UiO-MOFs in photocatalytic CO₂RR and potential future development directions are also presented. This review is intended to provide insights into CO₂ photoreduction using UiO-based materials and to encourage further research and development in this promising field.

This study explores the adoption of Virtual Reality (VR) in the Nigerian construction industry, with a focus on its potential benefits and associated challenges. Purposive and snowball sampling techniques were employed to select 52 construction professionals from Benin, Edo State,an emerging urban center with extensive construction activity. Adopting a quantitative approach, the research utilized a five-point Likert scale survey to assess perceptions of the benefits and barriers to VR adoption. The survey was pretested for clarity and reliability, and data were collected via the Qualtrics platform. The findings indicate that the key benefits of VR include improved task-technology alignment, enhanced workplace safety through virtual training, and more effective remote collaboration. VR was also found to enrich user experience and learning engagement by simulating high-risk scenarios to aid hazard prevention. Nevertheless, the study identifies several critical barriers to adoption, such as uncertainty regarding learning outcomes, technical disruptions, and high implementation costs. Despite these limitations, VR holds considerable promise for transforming training and professional development in the construction sector. To maximize its impact, the study recommends the development of customized training modules, technological improvements to enhance system reliability, and government support to mitigate implementation costs. Overall, VR has the potential to significantly improve training effectiveness, safety standards, and operational efficiency in the Nigerian construction industry, provided that the identified barriers are adequately addressed.

Amid growing global attention to occupational health and safety, the construction industry faces critical challenges associated with engineered stone, which emits high concentrations of respirable crystalline silica during processing and has been linked to severe lung diseases. In response, Australia enacted a comprehensive nationwide ban on engineered stone in July 2024. Drawing on media framing theory, this study analyzes public discourse and sentiment surrounding the ban by examining 7,198 comments collected from Reddit and YouTube. Through Latent Dirichlet Allocation, three dominant themes emerged: health risks and safety concerns, economic impacts and industry transition, and regulatory implementation. Sentiment analysis revealed that 55.5% of the comments expressed negative sentiment, mainly centered on economic concerns, while 21.3% were positive, emphasizing health benefits, and 23.1% were neutral. Economic impact frames predominated among negative comments, whereas health risk frames were more common in positive ones. These findings suggest that future policy communications should more effectively integrate narratives around both health protection and economic transition. This study contributes to the methodological development of sentiment analysis and offers practical insights for policy formulation and implementation.

Aims: Storytelling has evolved alongside human culture, giving rise to new media such as social robots. While these robots employ modalities similar to those used by humans, they can also utilize non-biomimetic modalities, such as color, which are commonly associated with emotions. As research on the use of colored light in robotic storytelling remains limited, this study investigates its integration through three empirical studies.

Methods: We conducted three studies to explore the impact of colored light in robotic storytelling. The first study examined the effect of emotion-inducing colored lighting in romantic storytelling. The second study employed an online survey to determine appropriate light colors for specific emotions, based on images of the robot’s emotional expressions. The third study compared four lighting conditions in storytelling: emotion-driven colored lights, context-based colored lights, constant white light, and no additional lighting.

Results: The first study found that while colored lighting did not significantly influence storytelling experience or perception of the robot, it made recipients felt more serene. The second study showed improved recognition of amazement, rage, and neutral emotional states when colored light accompanied body language. The third study revealed no significant differences across lighting conditions in terms of storytelling experience, emotions, or robot perception; however, participants generally appreciated the use of colored lights. Emotion-driven lighting received slightly more favorable subjective evaluations.

Conclusion: Colored lighting can enhance the emotional expressiveness of robots. Both emotion- driven and context-based lighting strategies are appropriate for robotic storytelling. Through this series of studies, we contribute to the understanding of how colored lights are perceived in robotic communication, particularly within storytelling contexts.

Bone defects represent a significant orthopedic challenge, with associated disorders continue to pose clinical difficulties. In the biomedical field, advancements in three-dimensional (3D) printing technology have established bone tissue engineering (BTE) scaffolds as a promising approach for effective treatment. These scaffolds not only provide structural support for cells but also serve as templates to guide bone tissue regeneration. In recent years, owing to their exceptional physicochemical properties, two-dimensional nanomaterials (2D NMs) have garnered increasing attention and have been widely explored as additives in the fabrication of BTE scaffolds. This review centers on the most recent developments in the combination of 2D NMs and 3D printing for BTE applications. It begins with a concise summary of the common synthesis and surface modification methods of 2D NMs. Then, it offers a comprehensive overview of recent advancements in their use within BTE. Finally, it discusses current challenges and future perspectives regarding the application of 2D NMs-based 3D-printed scaffolds in bone tissue regeneration.

The development of high-performance narrowband red organic light-emitting diodes (OLEDs) has garnered significant attention, offering both exciting opportunities and formidable challenges. In this study, we report the synthesis of a novel red thermally activated delayed fluorescence (TADF) molecule, 10,10',10''-([1,2,4]triazolo[1,5-a][1,3,5]triazine-2,5,7-triyltris(benzene-4,1-diyl))tris(10H-phenoxazine) (TPXZ-TAZTRZ), which integrates a highly electron-deficient triazolotriazine unit as the acceptor and three strongly electron-donating phenoxazine (PXZ) moieties as donors. TPXZ-TAZTRZ exhibits a small singlet-triplet energy gap, enabling a rapid reverse intersystem crossing rate. Additionally, it shows a broad emission spectrum peaking at 634 nm, spanning the yellow-to-red region. These features render TPXZ-TAZTRZ as an ideal TADF sensitizer for narrowband red fluorescent OLEDs. Accordingly, TPXZ-TAZTRZ was employed to sensitize the conventional fluorescent emitter DBP. The resulting TADF-sensitized fluorescence OLEDs (TSF-OLEDs) demonstrated efficient energy transfer from the TADF sensitizer to the emitter, effectively addressing the limitations previous encountered with TADF systems. The devices achieved high-performance pure red emission, with Commission International de l'Éclairage (CIE) coordinates of [0.67, 0.33], an emission peak at 612 nm, a narrow full width at half maximum (FWHM) of 27 nm, and a maximum external quantum efficiency of 16.2%.

Aims: This study aims to enhance the design of augmented reality (AR) technologies for remote collaboration by examining the complex relationships among individual factors (user characteristics), psychological and physiological states during AR-mediated remote collaboration, and outcomes within an Input-Mediator-Output (IMO) model. The goal is to evaluate how individual characteristics influence psychological and physiological experiences, as well as task performance, in AR-mediated collaboration.

Methods: We hypothesize and evaluate an IMO model and use correlation analyses to examine the relationships among person-related input variables (e.g., predispositions, traits, attitudes, states, and contextual factors), psychological and physiological emergent states, and performance-related output variables.

Results: Our results demonstrate that individual characteristics significantly influence subjective experiences, physiological responses, and task performance, emphasizing the critical role of individual differences, alongside task- and technology-related factors, in shaping collaboration experiences and performance. These findings highlight the importance of considering individual characteristics in the design of AR tools to optimize user well-being and performance outcomes.

Conclusion: Our study provides a foundational framework for understanding the interplay between individuals, tasks, and technology, underscoring the need for AR tools that align with user characteristics. It also lays the groundwork for future IMO research in AR-mediated remote collaboration, contributing to the development of more effective and health-promoting AR technologies.

Neuropeptides are crucial signaling molecules that regulate diverse physiological processes spanning growth, social behavior, learning, memory, metabolism, homeostasis, reproduction, and neural differentiation across both nervous and peripheral systems. Dysregulation of neuropeptides signaling is closely linked to various pathological conditions, such as neurological disorders, metabolic diseases, cardiovascular conditions, and even cancer, positioning them as potential therapeutic agents or targets for intervention. In recent years, research into neuropeptides has accelerated, with vast amounts of data continuously accumulating in multiple databases. However, the study of neuropeptides is often impeded by the need for extensive and time-consuming experimental investigations. As a result, computational tools have become essential for the rapid, large-scale identification of neuropeptides. This review systematically discusses neuropeptide-related databases and computational tools. These databases organize extensive data on neuropeptide sequences, structures, and functions. Among these, NeuroPep2.0, with 11,417 neuropeptide entries, is currently the most widely used dataset for neuropeptide prediction. Additionally, this review explores the application of computational approaches in neuropeptide prediction. While early methods predominantly relied on homologous sequence alignment and biochemical feature statistics, recent advances in machine learning have significantly enhanced prediction accuracy and efficiency. Tools such as NeuroPred-PLM and DeepNeuropePred, developed by our research group using protein language models, have substantially improved prediction performance. In conclusion, this review provides a comprehensive overview of current neuropeptide databases and computational tools, offering researchers a thorough survey of available resources and analytical methods, and emphasizing the necessity of continuous optimization to advance neuropeptide research and its therapeutic applications.

High-performance and long-lifetime blue organic light-emitting diodes (OLEDs) are crucial for meeting the demands of advanced display and lighting technologies. Despite high device efficiency has been achieved in blue OLEDs, development of high-performance and long-lifetime blue OLEDs still lag far behind their red/green counterparts due to the presence of long-lived high-energy triplet excitons and polarons. Given the critical role of charge and exciton management in both the emission and degradation processes of OLEDs, this review systematically summarizes strategies for suppressing charge leakage and exciton quenching, as well as for enhancing exciton utilization in blue fluorescent, phosphorescent, and thermally activated delayed fluorescent (TADF) OLEDs. In this context, we further discuss the roles of conventional fluorescent hosts, triplet-triplet annihilation/hot exciton hosts, TADF assistant hosts, phosphorescent assistant hosts, and exciplex/electroplex hosts in regulating charge and exciton dynamics in blue OLEDs. Additionally, the modification of emitting layer materials is highlighted as a key strategy for managing charge and exciton processes in efficient and stable solution-processed blue OLEDs. Based on current insights into the efficiency and operational stability of blue OLEDs, this review proposes feasible charge and exciton management strategies to address the current challenges.

The architecture, engineering and construction (AEC) industry is currently encountering numerous challenges and actively pursuing industrial upgrades through digital transformation. Digital twin (DT) technology aligns well with the industry's evolving needs due to its capabilities in data integration, intelligent decision-making, and effective management of project progress, efficiency, and quality. This study conducts a comprehensive literature review to analyze the strengths, weaknesses, opportunities, and threats (SWOT) associated with DT applications in the AEC sector. Additionally, field visits and semi-structured interviews were conducted on a selected construction project where DT was implemented, allowing for an in-depth examination of both its significant benefits and potential limitations. To further evaluate and prioritize the identified SWOT factors, a SWOT-AHP analysis was performed. The results indicate that the AEC industry has a strong interest in DT's advantages, particularly its potential to enhance resource allocation and process efficiency. Furthermore, the analysis reveals that the strategic quadrilateral occupies the largest area in the fourth quadrant (0.8554), with the strategic center of gravity located at (0.2321, -0.04135), suggesting that Strengths-Threats (ST) strategies should be implemented to support the future development and adoption of DT. Based on this strategic framework, the study formulates actionable development strategies to facilitate the effective integration and widespread adoption of DT in the AEC industry.

In pursuit of proactive safety management in construction, accurate and real-time recognition of workers' cognitive states is essential for crafting targeted interventions to minimize safety risks. Unlike conventional manual and qualitative methods, electroencephalogram (EEG) offers a reliable and objective solution for cognitive state recognition. Notably, efforts that integrate EEG with deep learning have exhibited significant advancements. Nevertheless, certain constraints, such as experimental costs and limited participants, have caused a scarcity of high-quality data, which has impeded performance in recognition tasks. Although transfer learning demonstrates its capability in addressing this challenge, the lack of relevant explorations into EEG-based cognitive state recognition remains a significant research gap. Therefore, customizing pre-trained deep learning models for these tasks would be beneficial. This study aims to develop pre-trained models to advance future studies in this domain through transfer learning, encompassing: 1) extracting extensive and accurately labeled EEG data from the DEAP dataset, 2) selecting appropriate network architectures and implementing pre-trained model development, and 3) collecting EEG data for mental fatigue recognition and evaluating model effectiveness. Rigorous evaluation suggests a substantial improvement in model performance, with test accuracy increasing from 63.19% to 92.29% by leveraging the pre-trained convolutional neural networks (CNN)—long short-term memory (LSTM) model. In conclusion, this study significantly contributes to enhancing safety management for construction workers through EEG by providing validated pre-trained models to address challenges of data scarcity. Future research may advance by exploring additional network architectures, increasing sample size, and considering more specific recognition tasks for model evaluation.

This study investigates the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) in construction projects to enhance efficiency, safety, and lifecycle monitoring. Despite the significant potential of BIM and IoT to facilitate the transformation of the construction industry, existing research lacks a comprehensive framework that addresses its interoperability challenges, data security concerns, and real-world implementation barriers. To address this gap, a multiple case study approach was employed, incorporating semi-structured interviews, document analysis, and real-world project evaluations. The results find that standardized protocols, data encryption, and modular IoT devices are essential for effective BIM-IoT adoption across different construction phases. The proposed framework offers a structured approach for construction teams to effectively utilize BIM and IoT, facilitating smarter and more sustainable project management. The contribution of this paper resides in the detailed analysis of the BIM-IoT applications, clearly demonstrating its advantages, such as improving building productivity and safety, and its potential to align with China's sustainable urban development goals. The study also provides a forward-looking prediction of the future development trend of BIM-IoT in China, offering valuable insights for construction project teams and decision makers. By demonstrating the structured framework and its practical application, this study provides guidance for the industry's transition to smarter and safer construction practices.

This paper explores two virtual child simulators, BabyX and the VR Baby Training Tool, which provide immersive, interactive platforms for child-focused research and training. These technologies address key ethical and practical constraints, enabling the systematic study of caregiver-infant interactions and the development of professional relational skills with young children. We examine their design, features, and applications, as well as their future trajectories, highlighting their potential to advance research and improve training methodologies.

The predominance of a linear economic model and the limited integration of circular strategies in the design and execution of building projects—particularly in the construction sectors of developing countries—have resulted in ongoing pressure on natural resources, high levels of waste generation, reduced productivity, and frequent time and cost overruns. Collectively, these issues contribute to unsustainable development, adversely impacting the social, economic, and environmental dimensions of sustainability. This study explores the perceptions of design professionals regarding the benefits, awareness, and implementation of Design for Deconstruction (DfD) within the Nigerian construction industry (NCI). Data were collected through a structured questionnaire distributed electronically to design experts in Nigeria's South-South geopolitical zone using a snowball sampling technique. With a 40.10% response rate and a reliability index above 0.800, the data were analysed using Exploratory Factor Analysis (EFA) and Partial Least Squares Structural Equation Modelling (PLS-SEM). Findings reveal that awareness of DfD is moderate, but its adoption remains low. EFA identified five key categories of DfD benefits: (1) business benefits, (2) economic benefits, (3) environmental benefits, (4) green certification and technology integration, and (5) social benefits. PLS-SEM results show that all five categories have a positive and significant influence on the decision to adopt DfD within the NCI. This study contributes to the theoretical advancement and practical understanding of circular construction practices, particularly DfD, with implications for reducing construction waste, improving resource efficiency, and supporting the achievement of Sustainable Development Goals (SDGs) 3, 9, 11, 12, and 13.

Digital twin (DT) technology is revolutionizing the architecture, engineering, construction, and operation (AECO) industry, driven by the advancements of Construction 4.0 (C4.0). Despite this, adopting DT in the AECO sector remains limited due to various barriers. This study, driven by four research questions (RQ), aims to advance DT adoption in the AECO industry under C4.0 using an integrated approach. The degrees of influence and importance ranking of barriers are assessed through the decision-making trial and evaluation laboratory (DEMATEL). The hierarchy and causal relationships among barriers are revealed through interpretative structural modeling (ISM). Then, barriers are divided into four clusters employing the cross-impact matrix multiplication applied to classification (MICMAC) method. The paper reveals that "lack of government financial and policy support" is the most critical barrier, "lack of trust and long-term perspective in DT" is the most significant direct-influence barrier, and "immature 3D engine technology" is the most fundamental barrier. By exploring interrelationships and prioritizing barriers, the study provides insights to enhance adopting DT in the AECO industry in the context of C4.0.

Energy performance contracting (EPC) has been implemented as a turnkey solution to enhance the energy efficiency of building systems and fixtures. The two most common types of EPC-guaranteed savings contracts and shared savings contracts-are widely applied in the industry, with their selection primarily depending on stakeholders' risk tolerance and the availability of external financing. EPC involves multiple key participants, including the government, third-party financiers, building owners, and energy service companies (ESCOs). The relationships among these stakeholders vary based on the contract type: in a guaranteed savings contract, the owner has a direct financial relationship with the third-party financier, whereas in a shared savings contract, the ESCO assumes this role. This paper provides a critical review of previous studies on EPC, categorizing them into four key areas: (1) challenges in EPC adoption, (2) critical success factors, (3) diffusion strategies from different stakeholder perspectives, and (4) a stakeholder relationship framework linking key success factors and strategies. By analyzing these aspects, this review aims to inform potential investors and contractors about essential considerations for EPC implementation while examining the stakeholder dynamics within EPC projects.

Supercapacitors, renowned for their high-power density, rapid charge/discharge capabilities, and exceptional cycling stability, have emerged as promising solutions for sustainable and efficient energy storage. Among various electrode materials, carbon materials stands out due to its abundance, excellent electrical conductivity, chemical stability and structural versatility. This review explores the design strategies, performance optimization, and the expanding applications of carbon-based electrodes for supercapacitors. We first analyze the key factors that impact the performance of carbon electrodes for supercapacitors, including pore structure, surface chemistry, electrical conductivity and nanoscale architecture. Subsequently, we provide an in-depth analysis of recent advancements in the rational design of carbon materials, focusing on strategies for optimizing pore architecture, functionalizing surfaces, enhancing conductivity and designing nanostructures. By addressing performance limitations, the review highlights strategies that have significantly improved the efficiency of carbon electrodes. Furthermore, we explore the practical applications of carbon-based supercapacitors in wearable electronics, self-powered devices, and implantable systems. Lastly, we discuss the challenges and opportunities associated by carbon-based electrodes from the perspective of electrode design and practical application.

Circularly polarized light (CPL) features electromagnetic vectors that rotate regularly in a plane perpendicular to the direction of propagation, transmitting optical chirality information that is imperceptible to human beings. CPL can be classified into the left-handed and right-handed circularly polarization light (L-/R-CPL), depending on whether the rotation direction is clockwise or anticlockwise, respectively. The ability to manipulate and characterize CPL is crucial for advancing various optical technologies, making the effective and direct detection of CPL extremely important. Breeding in the hotbed provided by the explosively increased chiral materials with CPL luminescence and strong circular dichroism (CD), CPL detectors are currently experiencing savage growth. Mainstream strategies can be divided into the leverage of photoactive materials with inherent chirality and the integration of chiral metamaterials with nonchiral photoactive materials. In this review, we not only highlight significant material innovations and detector architectures for CPL detection but also address the broader implications of these advancements. We discuss the challenges and future directions in this field, particularly focusing on how these developments could impact existing commodities, such as polarimetric imaging and security communications, and contribute to sustainability in technology through improved detection efficiency. Our goal is to inspire further promising developments in CPL photodetectors and encourage a broader application spectrum.

Magnetorheological fluid (MRF) has broad application prospects in the field of engineering vibration damping due to its magnet-sensitive characteristics. However, owing to its dependence on external electric or magnetic fields, devices based on MRF can't work once the power is turned off. Here we developed a magnetorheological shear thickening fluid (MRSTF) with both the magnet-sensitive and rate-sensitive characteristics. The shear thickening effect of MRSTF was examined, revealing that the critical shear rate ${\dot{\gamma }}_c$ of the suspension gradually decreases from 79.4 s^-1 to 30.2 s^-1 and the shear thickening power β of the suspension gradually decreases from 0.27 to 0.19 as the carbonyl iron powder (CIP) content increases from 0 to 30 vol.% when the mass fraction of SiO₂ is 54 wt.%. Besides, the MRSTF-based dampers were tested under different frequencies and amplitudes. The results show that the MRSTF-based dampers still exhibit the energy dissipation characteristics in the absence of magnetic field because of the rate-sensitive property of MRSTF. Under the condition of an applied magnetic field, the dampers exhibit stronger energy dissipation characteristics due to the magnetic-sensitive characteristics of MRSTF. Concurrently, the dissipation capacity of the dampers increases with the increase of frequency and amplitude. This work provides an approach to enhance the mechanical properties of dampers and widen the application field.

Aims: To give a comprehensive understanding of current research on immersive empathic computing, this paper aims to present a systematic review of the use of Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR) technologies in empathic computing, to identify key research trends, gaps, and future directions.

Methods: The PRISMA methodology was applied using keyword-based searches, publishing venue selection, and citation thresholds to identify 77 papers for detailed review. We analyze these papers to categorize the key areas of empathic computing research, including emotion elicitation, emotion recognition, fostering empathy, and cross-disciplinary applications such as healthcare, learning, entertainment and collaboration.

Results: Our findings reveal that VR has been the dominant platform for empathic computing research over the past two decades, while AR and MR remain underexplored. Dimensional emotional models have influenced this domain more than discrete emotional models for eliciting, recognizing emotions and fostering empathy. Additionally, we identify perception and cognition as pivotal factors influencing user engagement and emotional regulation.

Conclusion: Future research should expand the exploration of AR and MR for empathic computing, refine emotion models by integrating hybrid frameworks, and examine the relationship between lower body postures and emotions in immersive environments as an emerging research opportunity.

Incorporation of two dimensional (2D) materials into a polymer matrix is an efficient way to prepare high performance coatings. Here we report the in-situ mechanical exfoliation of graphite in a mixture of hydroxyethylacrylate terminated polybutadiene urethane (HTPU) prepolymer and reactive diluent. The mixture containing exfoliated graphite nanoplatelets (GN) is directly used to prepare UV cured composite resins (G/HTPU) with various GN concentrations. Various techniques, such as scanning electron microscopy (SEM), Raman, atomic force microscopy (AFM), have been used to characterize the GN, confirming that few-layered GN are obtained after the in-situ mechanical exfoliation. The incorporation of GN exerts little effect on the curing of the coatings with a gelation around 95%, but greatly enhances the elastic modulus. Tafel polarization curves, electrochemical impedance spectroscopy (EIS) and salt-spray testing were conducted to comparatively evaluate the G/HTPU coatings with different GN loadings. The results indicate that the incorporation of GN greatly improves the corrosion resistance of the HTPU UV coatings. The self-corrosion current density (I _corr) and the charge transfer resistance (R _c) of G/HTPU-2 (0.2% GN loading) are greatly reduced to 1.03 × 10^-8 A·cm^-2 and increased by two magnitudes, respectively, compared to those of the parent HTPU coating. Additionally, the G/HTPU-2 coating with thickness of 100 μm can protect galvanized sheet against 0.5 mol/l sulfuric acid for at least 24 h. The practical application in protection of electronics was illustrated by coating the G/HTPU-2 on a standard printed circuit board (PCB, IPC-B-24A). No corrosion was observed after it was immersed in an artificial sweat solution even under open-circuit voltage of 12 V for 72 h and then 24 V for 48 h.

White-color organic light-emitting diodes (WOLEDs) have aroused wide interests for future lighting because of low energy consumption in principle, while still suffer from the degradation by Joule heating and polaron-induced quenching under high luminance caused by high current in practical. To obtain high current efficiency, which means high luminance at low current density, tandem WOLEDs with multiple electroluminescence (EL) units connected in series with charge generation layers (CGLs) have been developed. Here we report the improved hybrid tandem WOLED with two EL units, EL1 and EL2, where EL1 and EL2 generate fluorescent blue and phosphorescent yellow emissions from a metal-free material BCzVBi and a Ir-complex (fbi)₂Ir(acac), respectively, with CGL composed of a HATCN/NPB bilayer. The white-light emitting device shows the maximum current efficiency and power efficiency of 60.2 cd/A and 29.6 lm/W, respectively, without out-coupling structure. A low driving voltage of 6.5 V at 1,000 cd/m² is realized, which is an important value because such a luminance is required for actually employed lighting device. The blue emission appears from 5.4 V, and the light color changes from yellowish to bluish white during 5.4 V to 13.4 V, which could be explained by the energy level structures of the device and proved by the J-V characteristics. Additionally, this tandem WOLED also shows photovoltaic effect. This work provides a design of room light source whose color temperature could be tuned through applied voltage when needed, and a display system for low-current operated amorphous Si-TFT due to its high current efficiency.

Blue emitters are highly desired in organic light-emitting diodes (OLEDs), but their electroluminescence efficiencies and roll-offs are always less than satisfactory. In this work, a triazine-based deep-blue emitter (2PhCzTRZ-Cz) is designed and synthesized. It prefers high thermal stability with a decomposition temperature of up to 543 °C, and possesses strong deep-blue photoluminescence. The doped OLEDs using 2PhCzTRZ-Cz as an emitter attain deep-blue lights at 418-424 nm, with high maximum external quantum efficiencies (η_extS) of 4.46-5.68%, maximum luminances of 2,820-7,400 cd m^-2, CIE_y values < 0.1 and small efficiency roll-offs. In addition, multiple-resonance thermally activated delayed fluorescence OLED by using 2PhCzTRZ-Cz as host realizes a high maximum η_ext of 21.5%, significantly higher than those of the device based on traditional mCBP host (12.9%). These outstanding performances demonstrate the great potential of 2PhCzTRZ-Cz as an emitter and host for OLEDs.