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

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

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Lab-on-a-Chip (LOC), AI-driven automation, scalable manufacturing, IoT connectivity, quality assurance, industry-academia collaboration, digital twins, machine learning, regulatory compliance, real-time monitoring

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.