Unraveling one pot lactose conversion to lactic acid and HMF over Sn-Er/ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1249" altimg="si26.svg"> <mml:mi>γ</mml:mi> </mml:math> -Al2O3
Bibliographic record
Abstract
Residual whey permeate from cheese making contains 5 % lactose, and so is a potential feedstock for platform chemicals like lactic acid (LA), and hydroxymethylfurfural (HMF). Lactose hydrolyzes to glucose, and galactose monosaccharides as the first step followed by isomerization to fructose. Fructose either dehydrates to yield HMF or converts to C3 intermediates (glyceraldehyde, dihydroxyacetone) through a retro-aldol condensation. Those intermediates dehydrate to form α , β -unsaturated species, which convert to pyruvaldehyde (PVA) through keto-enol tautomerization. Adding one molecule of water leads to the formation of a diol, which tautomerizes to form LA. Heterogeneous catalysts come as a potential economic alternative to enzymes and homogeneous catalysts, with lower capital requirements (smaller reactors), and fewer separation steps. Here, an erbium catalyst (0.15 g g −1 Er) on a γ -Al 2 O 3 support we synthesized, converted all the lactose at 170 °C, and lactic acid reached a maximum yield of 23 % after 30 min, at 70 rpm. XRF, XRD and XPS confirmed that metallic active sites (Sn and Er) successfully incorporated the γ -Al 2 O 3 in their oxide form. SEM-EDS and BET revealed that Sn is mainly at the surface of the support, while Er is embedded in the core, below Al, and O. Monometallic Er catalyst was more active than bimetallic Sn-Er catalyst, due to a hindering of Er species by Sn oxide at the surface. Experimental data matched with a model, which captures the reaction dependency on time, temperature, and metal loading, to form lactic acid. This study highlights the role of tin, erbium and operation parameters in the complex mechanism of lactose to lactic acid, and hydroxymethylfurfural.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".