A methodological combined framework for roadmapping biosensor research: a fault tree analysis approach within a...

Area of Science:

• Biosensor Technology and Nanotechnology
• Research and Development Roadmapping
• Scientific Innovation Management

Background:

• Biosensor technology has advanced significantly since the 1960s, with notable success in glucose sensors and artificial pancreas development.
• Despite extensive academic research and growing university groups, the industry underutilizes university-produced biosensor knowledge.
• A gap exists between academic research output and marketability in the biosensor field.

Purpose of the Study:

• To develop a combined methodological framework for roadmapping university research output in biosensor technology.
• To identify internal barriers hindering the commercialization of biosensor innovations.
• To prospectively analyze future trajectories for biosensor development, emphasizing manufacturability.

Main Methods:

• Utilized the Analytic Hierarchy Process (AHP) principles for framework development.
• Modeled internal barriers (drawbacks, limitations) using fault tree analysis (FTA) with fuzzy reasoning for uncertainty.
• Validated the methodology retrospectively with ion-selective field-effect transistor (ISFET)-based biosensors and prospectively with membrane biosensors.

Main Results:

• The proposed framework offers a novel approach to mapping university biosensor research, differing from existing market roadmaps.
• Retrospective and prospective analyses revealed that future biosensor trajectories lean towards nanotechnology, specifically nanofabrication and nano-bioinformatics.
• Emphasis is placed on controlling self-assembly processes and the thermodynamics of bioelement-lipid interactions for enhanced sensitivity.

Conclusions:

• The study identifies a divergence between academic research scope and market perspectives in biosensor development.
• Nanotechnology, particularly nanofabrication and nano-bioinformatics, represents a promising future direction for biosensor innovation.
• Controlling natural self-assembly and bioelement-lipid interactions is crucial for advancing biosensor sensitivity and performance.