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Updated: May 5, 2026

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

Navid Rabiee1, Mohammad Rabiee2

  • 1Department of Biomaterials, Saveetha Dental College and Hospitals, SIMATS, Saveetha University, Chennai, 600077, India. nrabiee94@gmail.com.

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|January 5, 2026
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Summary
This summary is machine-generated.

Quantum-inspired fractal sustainability optimization (QIFSO) offers a new way to design sustainable biosensors. This advanced method improves development timelines by 60% and enhances key performance metrics for biosensing technologies.

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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).