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From copper-chromite to MXenes: A century of furfural hydrogenation – Experiments, DFT, machine learning, and path forward

Author

Listed:
  • Mahmood, Ayyaz
  • Li, Sijie
  • Liu, Jin
  • Madduluri, Venkata Rao
  • Gao, Xi

Abstract

Furfural hydrogenation is a key step in converting biomass-derived platform molecules into value-added chemicals and fuels. This review covers experimental and computational work from 1929 to 2025, starting with early copper and nickel catalysts and moving to today's bimetallic alloys, MXene-supported systems, and Pt/aluminosilicates. We examine how metal composition, support acidity, solvent choice, and hydrogen pressure steer selectivity toward furfural alcohol (FA), 2-methylfuran (2-MF), tetrahydrofurfural alcohol (THFA), or the two pentanediols. Density functional theory (DFT) calculations, including our recent work, reveal adsorption geometries, activation barriers, and branching pathways, while machine-learning (ML) models predict high FA selectivity for optimized bimetallic systems. A unified scheme is proposed that integrates initial carbonyl hydrogenation, side-chain hydrogenolysis, full ring saturation, and ring-opening routes, whether from THFA or directly from FA (our patented catalysts and recent Pt/aluminosilicate results). Key factors controlling selectivity are discussed in detail. Catalyst deactivation, noble-metal cost, and scale-up remain hurdles; non-precious metals, robust supports, and combined theory-experiment loops offer practical fixes. Techno-economic viability is highlighted, with minimum selling prices (MSP) of ∼$1300 t−1 for FA (single-step) and higher for diols due to complexity, alongside lifecycle benefits (near-zero or net-negative CO2 with renewable H2 for FA production). The discussion concludes with concrete directions for selective, stable, and industrially viable furfural upgrading.

Suggested Citation

  • Mahmood, Ayyaz & Li, Sijie & Liu, Jin & Madduluri, Venkata Rao & Gao, Xi, 2026. "From copper-chromite to MXenes: A century of furfural hydrogenation – Experiments, DFT, machine learning, and path forward," Renewable and Sustainable Energy Reviews, Elsevier, vol. 232(C).
  • Handle: RePEc:eee:rensus:v:232:y:2026:i:c:s1364032126001024
    DOI: 10.1016/j.rser.2026.116803
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