A Phenomenological Geometric Classification of Recurrent Mass Windows in LENR Transmutation Data

Several independent LENR transmutation data sets have reported the recurrent appearance of five relatively narrow mass regions across the periodic table. This empirical pattern, emphasized in recent reviews, remains without a widely accepted interpretation. Similar mass intervals have also been noted in compilations of electron-screening potentials obtained from low-energy deuteron fusion experiments. In this technical note, we adopt a deliberately phenomenological approach. We introduce a dimensionless geometric parameter defined as the ratio between the nuclear diameter and the electron Compton wavelength, and anchor five reference values to the empirically observed mass windows identified by Nagel, namely A = 12, 32, 66, 120, and 198. A simple distance metric Δ(A) is then used to quantify how closely each nucleus approaches these reference values. When applied systematically, this geometric classification provides a compact way to organize reported enhancement regions and extended intervals of relative suppression. No microscopic mechanism or reaction kinetics are assumed. The purpose of the present work is not to derive the origin of the mass windows, but to offer a reproducible and testable framework for organizing, comparing, and potentially predicting structure in LENR transmutation data, enabling systematic identification of preferred mass regions and their relation to material-dependent conditions.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-24
DOI
https://doi.org/10.5281/zenodo.20205683
Primary Topic
Cold Fusion and Nuclear Reactions
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

A Phenomenological Geometric Classification of Recurrent Mass Windows in LENR Transmutation Data

Mario Menichella, Guido Parchi, Ugo Abundo, Federico Galli
Zenodo (CERN European Organization for Nuclear Research)
Cold Fusion and Nuclear Reactions
preprint

A Phenomenological Geometric Classification of Recurrent Mass Windows in LENR Transmutation Data

Mario Menichella, Guido Parchi, Ugo Abundo, Federico Galli
preprint en

Abstract

Several independent LENR transmutation data sets have reported the recurrent appearance of five relatively narrow mass regions across the periodic table. This empirical pattern, emphasized in recent reviews, remains without a widely accepted interpretation. Similar mass intervals have also been noted in compilations of electron-screening potentials obtained from low-energy deuteron fusion experiments. In this technical note, we adopt a deliberately phenomenological approach. We introduce a dimensionless geometric parameter defined as the ratio between the nuclear diameter and the electron Compton wavelength, and anchor five reference values to the empirically observed mass windows identified by Nagel, namely A = 12, 32, 66, 120, and 198. A simple distance metric Δ(A) is then used to quantify how closely each nucleus approaches these reference values. When applied systematically, this geometric classification provides a compact way to organize reported enhancement regions and extended intervals of relative suppression. No microscopic mechanism or reaction kinetics are assumed. The purpose of the present work is not to derive the origin of the mass windows, but to offer a reproducible and testable framework for organizing, comparing, and potentially predicting structure in LENR transmutation data, enabling systematic identification of preferred mass regions and their relation to material-dependent conditions.

Zenodo (CERN European Organization for Nuclear Research)
The Future University (SD), Sapienza University of Rome (IT)
Cold Fusion and Nuclear Reactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.