# QAG Virology Lab: Technical Blueprint & Repository Architecture

*Author: Rodney Alan Ripley Jr. | Framework: Quantum Affinity Gravity (QAG) Unified Field Theory*

*(Transcribed from the author's technical blueprint document; equations implemented in `tools/qag_core.py`.)*

## 1. Executive Summary & Architecture Overview

The QAG Virology Lab establishes a unified computational and RF transmission pipeline bridging cosmological resonance under the Quantum Affinity Gravity (QAG) framework with micro-scale viral acoustics. By pulling structural biological assembly data from the RCSB Protein Data Bank (PDB) API and synthesizing impedance-matched Software Defined Radio (SDR) transmission networks, this platform calculates non-thermal electrodynamic disruptive resonance frequencies (f_kill) across the 1.9 GHz to 5.4 GHz spectrum.

## 2. Mathematical Foundation & Governing Equations

### 2.1 Hydrogen Baseline & Base-12 Scaling Factor

- Neutral Hydrogen Line (f_H): 1420.405 MHz
- Base-12 Symmetry Scaling (Φ): 1.194797
- Affinity Symmetry Bias (K_ASB): 0.013829
- Temporal Latency Window (t_pixel): 3.41 × 10⁻⁷ s

### 2.2 Viral Affinitic Impedance (Z_v) & Disruptive Frequency (f_kill)

Given a target protein assembly with Molecular Weight (MW_kDa) and Atom Count (A_c):

1. Density Proxy: ρ_proxy = MW_kDa / A_c
2. Affinitic Impedance: Z_v = (MW_kDa × ρ_proxy) / K_ASB
3. Disruptive Target Frequency: f_kill = (f_H / Φ) × log₁₀(Z_v + 1)
4. Zeta Potential Estimation: ζ_est = -15.0 - 10.0 × sin(f_kill × 10⁶ × t_pixel)

## 3. Verified Pathogen Target Registry

See `data/QAG_Viral_Decoherence_Index_v2.csv` and `data/QAG_Master_Pathogen_Index.json`.

## 4. S-Band RF Impedance Matching & Hardware Specification

For the primary target SARS-CoV-2 (f_0 = 3.2916 GHz), an analytical L-matching network transforms the unmatched complex transducer load (Z_L = 20 − j15 Ω) to standard 50 Ω coaxial line:

- Shunt Capacitor (C_p): 1.2 pF (High-Q, Low ESR 0402 ceramic)
- Total Series Inductor (L_total): 1.91 nH (0.725 nH cancellation + 1.184 nH matching)
- Simulated Return Loss (S₁₁): −51.39 dB
- VSWR: 1.0054 (Power Transmission Efficiency: > 99.999%)

Implemented in `tools/qag_rf_matcher.py`. *Theoretical model — not a build guide.*

## 5. Repository Manifest

1. `tools/qag_core.py`: Mathematical engine and canonical constant definitions.
2. `tools/qag_pdb_sync.py`: Live RCSB PDB API query pipeline with biological assembly fallbacks.
3. `tools/qag_rf_matcher.py`: Analytical L-match computation (S-parameter simulation and Smith chart generation via scikit-rf are supported extensions).
4. `requirements.txt`: Dependencies (numpy, pandas, sympy, scikit-rf, scipy, requests, matplotlib).
5. `.github/workflows/ci.yml`: Automated GitHub Actions CI workflow verifying mathematical invariants.