This Research Topic (RT) aims to bridge the gap between cutting-edge academic research and real-world industrial applications in Lab-on-a-Chip (LOC) technologies. Bringing together leaders from academia, R&D, and industry, the RT will showcase how LOC is transforming sectors such as:

- Pharmaceuticals & drug screening
- Diagnostics & point-of-care testing
- Food and beverage quality control
- Environmental monitoring
- Advanced manufacturing & process automation

By highlighting AI-driven automation, scalable production, and integration with IoT/Industry 4.0 systems, this topic will further enhance dialogue, collaborative innovation, and technology transfer between research labs and industry practitioners.

Key Themes:

- AI-Driven Automation in LOC Platforms: Advances in automated data acquisition, analysis, and real-time decision-making. See Nature's review: Trends in AI and automation in microfluidics
- Scalable Manufacturing & Commercialization: Case studies and methods for transitioning LOC prototypes to industrial production and market adoption.
- Integrated Sensing & IoT Connectivity: Embedding wireless, cloud-enabled monitoring and distributed analytics into chips for smart manufacturing.
- Quality Assurance, Regulatory, and Standardization: Harmonization strategies and digital quality management leveraging AI/ML.
- Success Stories: Industry-academia collaborations that have led to breakthrough LOC deployments.

This Research Topic seeks to accelerate the translation of advanced Lab-on-a-Chip research into high-impact industrial and commercial applications. Submissions are invited that:

- Present technology-driven automation solutions for sample processing, assay optimization, and high-throughput data analytics, reducing manual interventions and increasing reproducibility.
- Demonstrate how digital twins, simulation, and computational modeling streamline LOC design, performance prediction, and manufacturing scale-up.
- Describe the merging of LOC platforms with cloud connectivity, edge computing, and IoT - enabling real-time monitoring, predictive maintenance, and seamless data integrity across distributed production sites.
- Showcase the use of robotic microfluidics and smart instrumentation to enable continuous, unattended operation - achieving 24/7 industrial analytics or manufacturing with minimal labor.
- Integrate machine learning and big data analytics to reveal insights from complex biological, chemical, or clinical datasets generated by LOC systems.
- Explore automated quality control using machine vision, AI-driven fault detection, and in-line statistical process control, driving regulatory compliance and reliability.
- Highlight efforts in standardization, modular design, and plug-and-play interoperability - facilitating rapid system integration, upgrades, and scaling for diverse industrial environments.

To empower both academic and industrial users, the RT will highlight:

- AI Optimization Engines: Deep learning and reinforcement learning solutions for protocol optimization, process scheduling, and yield improvement.

- Self-Adaptive LOC Platforms: Systems that automatically adapt workflows in response to real-time sensor feedback, minimizing errors and downtime.

- Digital Engineering & Automation Pipelines: End-to-end software tools - from CAD for LOC layout, to automated experiment setup, remote monitoring, and digital documentation - enabling faster, “hands-off” R&D and deployment.

- Open Access Repositories & Digital Twins: Promoting shared, AI- and simulation-ready datasets/models to accelerate cross-sector innovation and reproducibility.

o Interfacing with Robotics: Use of programmable robots for reagent handling, sample sorting, or cartridge exchange, limiting manual labor and contamination risk.

- Secure, Scalable Data Infrastructure: Mechanisms for collecting, warehousing, and mining LOC system data for both operational insights and regulatory traceability.

Contributions demonstrating these technologies - especially with clear metrics for decreased manual effort, enhanced reliability, or new business/clinical value - will be particularly encouraged.

Suggested Manuscript Types

- Original industrial case studies
- Reviews of industry standards and trends
- Technical papers on automation, integration, and scaling
- Perspective articles and policy discussions
- Short communications showcasing rapid prototyping or deployment successes

Are you interested in contributing a manuscript to this Research Topic?

From Lab to Fab: Accelerating Industrial Innovation and Automation with Lab-on-a-Chip Technologies