# Sieving Hydrogen Isotopes via Machine Learning Assisted Chemical Vapor Deposition (CVD) of High‐Quality Monolayer Hexagonal Boron Nitride (h‐BN) on Iron Foils

## TL;DR

Researchers developed a scalable method to produce high-quality h-BN films using machine learning, enabling efficient separation of hydrogen isotopes.

## Contribution

A machine learning-assisted Fe-catalyzed CVD process for scalable, high-quality h-BN synthesis with near-pristine H+/D+ selectivity.

## Key findings

- Fe-catalyzed CVD produces large-area monolayer h-BN with H+/D+ selectivity of ≈8.45.
- Machine learning optimizes CVD parameters to minimize multilayer formation and secondary nuclei.
- The method outperforms Cu-catalyzed CVD h-BN in H+/D+ selectivity.

## Abstract

Atomically thin two‐dimensional (2D) ceramics, such as monolayer hexagonal boron nitride (h‐BN), present potential for disruptive advances in separations. However, sub‐atomic scale separation of hydrogen isotopes (H+/D+) require near pristine 2D material membranes, and scalable synthesis of such high‐quality h‐BN comparable to mechanically exfoliated crystals remains a significant challenge. Here, we report a scalable Fe‐catalyzed chemical vapor deposition (CVD) process for bottom‐up synthesis of large‐area, high‐quality monolayer h‐BN films, overcoming key limitations of conventional ammonia‐based routes. By leveraging mechanistic insights and higher CVD temperatures, we suppress multilayer formation and achieve uniform monolayer h‐BN coverage on commercially available Fe foils. Machine learning enables systematic exploration of the complex, multi‐dimensional CVD parameter space (growth time, temperature, precursor temperature, multilayer faction, coverage), providing data‐driven approaches to visualize and identify process regimes facilitating predominantly monolayer h‐BN growth with minimal secondary nuclei/ad‐layers. The optimized Fe‐catalyzed CVD h‐BN membranes show high‐quality as observed by proton/deuteron (H+/D+) selectivity ≈8.45, approaching the highest quality benchmark of mechanically exfoliated h‐BN (H+/D+ selectivity ≈10) as well as significantly outperforming Cu‐catalyzed CVD h‐BN membranes (H+/D+ selectivity ≈3.62, control selectivity ≈1.7). Our work provides a scalable cost‐effective route for high‐quality monolayer h‐BN synthesis for sub‐atomic scale separations (H+/D+) and demonstrates the broader potential of machine learning‐guided optimization of CVD for advancing synthesis of 2D materials.

Sub‐atomic scale separation of hydrogen isotopes (H+/D+) require near pristine h‐BN membranes, and scalable synthesis of such high‐quality h‐BN comparable to mechanically exfoliated crystals remains a significant challenge. This study reports a scalable Fe‐catalyzed Machine‐Learning Enabled Chemical vapor deposition process for bottom‐up large‐area, high‐quality monolayer h‐BN film synthesis and demonstrate H+/D+ separation with selectivity approaching mechanically exfoliated h‐BN.