Quantum-inspired fractal sustainability optimization for next-generation biosensor development

Area of Science:

• Biosensing Technologies
• Sustainability Science
• Quantum Information Theory
• Multifractal Analysis

Background:

• Conventional linear frameworks inadequately assess sustainability in complex biosensor designs.
• Existing methods overlook intricate interdependencies among sustainability parameters.
• A novel approach is needed to capture the multidimensional nature of biosensor sustainability.

Purpose of the Study:

• Introduce Quantum-Inspired Fractal Sustainability Optimization (QIFSO) for biosensor design.
• Develop a multidimensional assessment framework for biosensing technologies.
• Enhance sustainability and accelerate innovation in biosensor development.

Main Methods:

• Integrated quantum information theory and multifractal analysis.
• Transformed 15 sustainability parameters into a 3D state space (Parameter Resilience, Sustainability Momentum, Criticality Coefficient).
• Utilized hierarchical clustering (k-means) and multifractal analysis to identify sustainability regimes and parameter interdependencies.

Main Results:

• Identified four universal sustainability regimes across biosensor applications.
• Demonstrated non-integer dimensionality of the parameter space (Dq = 2.69 ± 0.05), explaining limitations of linear models.
• Established a power law relationship (CC = 0.45 × PR^-1.68 + 0.19, R^2 = 0.84) for predictive optimization.
• Validated QIFSO via in silico case studies, indicating 18-52% potential sustainability improvements.

Conclusions:

• QIFSO provides a robust, multidimensional framework for sustainable biosensor design.
• The methodology significantly reduces development timelines by 60% and enhances performance.
• QIFSO demonstrates broad applicability and accelerates sustainable innovation across diverse research organizations (92% implementation success rate).