Comprehensive Study Addressing the Challenge of Efficient Electrocatalytic Biomass Upgrading of 5‐(Hydroxymethyl)Furfural (HMF) with a CH<sub>3</sub>NH<sub>2</sub> Ionic Liquid on Metal‐Embedded Mo<sub>2</sub>B<sub>2</sub> MBene Nanosheets
Bibliographic record
Abstract
Abstract Amine‐containing derivatives are important intermediates in drug manufacturing; sustainable synthesis of amine compounds from green carbon‐based biomass derivatives has attracted increasing attention, especially the reductive amination of biomass molecules via electrochemical upgrading. To achieve efficient reductive amination of 5‐(hydroxymethyl)furfural (HMF) via electrocatalytic biomass upgrading, this work proposes a new HMF biomass upgrading strategy based on metal supported on Mo 2 B 2 MBene nanosheets using a density functional theory comprehensive study. HMF and methylamine (CH 3 CH 2 ) can be reduced to 5‐(hydroxymethyl) aldiminefurfural (HMMAMF) via electrocatalytic biomass upgrading, which is identified as a promising technology to produce pharmaceutical intermediates. Based on the proposed reaction mechanisms of HMF reductive amination, this work performs a systematic study of HMF amination to HMMAMF using an atomic model simulation method. This study aims to design a high‐efficiency catalyst based on Mo 2 B 2 @TM nanosheets via the reductive amination of 5‐HMF and provide insights into the intrinsic relation between thermochemical and material electronic properties and the role of dopant metals. This work establishes the Gibbs free energy profiles of each reaction HMF Biomass Upgrading on Mo 2 B 2 systems and obtained the limiting potentials of the rate‐determining step, which included the kinetic stability of dopants, HMF adsorbability, and the catalytic activity and selectivity of the hydrogen evolution reaction or surface oxidation. Furthermore, charge transfer, d‐band center ( ε d ), and material property ( φ ) descriptors are applied to establish a linear correlation to determine promising candidate catalysts for reductive amination of HMF. The candidates Mo 2 B 2 @Cr, Mo 2 B 2 @Zr, Mo 2 B 2 @Nb, Mo 2 B 2 @Ru, Mo 2 B 2 @Rh, and Mo 2 B 2 @Os are suitable high‐efficiency catalysts for HMF amination. This work may contribute to the experimental application of biomass upgrading catalysts for biomass energy and guide the future development of biomass conversion strategies and utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".