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This study compares the fatigue performance and biocompatibility of as-built and chemically etched Ti-6Al-4V alloys in TPMS-gyroid and stochastic structures fabricated via Powder Bed Fusion Laser Beam (PBF-LB). This study aims to understand how complex lattice structures and post-manufacturing treatment, particularly chemical etching, affect the mechanical properties, surface morphology, fatigue resistance and biocompatibility of these metamaterials for biomedical applications.

Selective Laser Melting (SLM) technology was used to fabricate TPMS-gyroid and Voronoi stochastic designs with three different relative densities (0.2, 0.3 and 0.4) in Ti-6Al-4V ELI alloy. The as-built samples underwent a chemical etching process to enhance surface quality. Mechanical characterization included static compression and dynamic fatigue testing, complemented by scanning electron microscopy (SEM) for surface and failure analysis. The biocompatibility of the samples was assessed through in-vitro cell viability assays using the Alamar Blue assay and cell proliferation studies.

Chemical etching significantly improves the surface morphology, mechanical properties and fatigue resistance of both TPMS-gyroid and stochastic structures. Gyroid structures demonstrated superior mechanical performance and fatigue resistance compared to stochastic structures, with etching providing more pronounced benefits in these aspects. In-vitro biocompatibility tests showed high cytocompatibility for both as-built and etched samples, with etched samples exhibiting notably improved cell viability. The study also highlights the importance of design and post-processing in optimizing the performance of Ti64 components for biomedical applications.

The comparative analysis between as-built and etched conditions, alongside considering different lattice designs, provides valuable information for developing advanced biomedical implants. The demonstration of enhanced fatigue resistance and biocompatibility through etching adds significant value to the field of additive manufacturing, suggesting new avenues for designing and post-processing implantable devices.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.