Why Classical Physics Could Not Unify Gravity: The Missing Density Constant

Classical physics divides nature into two regimes: a quantum world governed by the Planck constant h, and a gravitational world governed by the Newtonian constant G. Despite one century of effort, the two regimes remain formally incompatible because neither framework contains a parameter that characterizes the density of space itself. In this paper, I argue that a fundamental density scale—denoted \mu_g—is logically necessary for any unified physical theory. Building on recent geometric models where space evolves as a helical motion, I show that the familiar constants G, \hbar, and c correspond to radial, torsional, and axial components of the same geometric motion. From this perspective, h cannot serve as the universal constant of unification because it measures action, not density. The density constant \mu_g provides the missing bridge: it allows gravitational coupling to be expressed as a property of space itself and reveals that the quantum constant \hbar is also a geometric quantity, proportional to \mu_g c \ell_\Omega^2. This analysis explains why classical and quantum physics have remained disjoint: both lack the necessary density scale. I conclude by proposing that \mu_g is not an additional assumption but a fundamental property of space that any geometric unified field theory must incorporate.

References to the prior geometric theory by Zhang Xiangqian and Xu Yuchuan are included to situate this work within the emerging framework of helical-space physics.

Brief Biography

Lynn Lou Beran is a Senior IT Security Analyst and an author, translator, and editor working at the intersection of science, engineering, and communication. She translated and edited multiple editions of Zhang XiangQian’s Unified Field Theory and co-authored recent manuscripts developing a geometric, motion-based view of physical reality.

Lynn’s broader work focuses on making advanced ideas accessible without sacrificing rigor, and on connecting conceptual insight to testable predictions and practical tools.