Evidence map›Paper›PMID 40806342›Full record

ReviewInternational journal of molecular sciences2025

Process Intensification Strategies for Esterification: Kinetic Modeling, Reactor Design, and Sustainable Applications.

Kim Leonie Hoff, Matthias Eisenacher

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kim Leonie HoffCircular Transformation Lab, Technische Hochschule Köln, Campus Leverkusen, 51379 Leverkusen, Germany.
Matthias EisenacherCircular Transformation Lab, Technische Hochschule Köln, Campus Leverkusen, 51379 Leverkusen, Germany.ORCID 0000-0002-9175-9914

Funding

Federal Ministry of Education and Research 031B1170F
6 · The paper itself

Abstract

Esterification is a key transformation in the production of lubricants, pharmaceuticals, and fine chemicals. Conventional processes employing homogeneous acid catalysts suffer from limitations such as corrosive byproducts, energy-intensive separation, and poor catalyst reusability. This review provides a comprehensive overview of heterogeneous catalytic systems, including ion exchange resins, zeolites, metal oxides, mesoporous materials, and others, for improved ester synthesis. Recent advances in membrane-integrated reactors, such as pervaporation and nanofiltration, which enable continuous water removal, shifting equilibrium and increasing conversion under milder conditions, are reviewed. Dual-functional membranes that combine catalytic activity with selective separation further enhance process efficiency and reduce energy consumption. Enzymatic systems using immobilized lipases present additional opportunities for mild and selective reactions. Future directions emphasize the integration of pervaporation membranes, hybrid catalyst systems combining biocatalysts and metals, and real-time optimization through artificial intelligence. Modular plug-and-play reactor designs are identified as a promising approach to flexible, scalable, and sustainable esterification. Overall, the interaction of catalyst development, membrane technology, and digital process control offers a transformative platform for next-generation ester synthesis aligned with green chemistry and industrial scalability.

Indexed as

BioreactorsBiocatalysisCatalysisEnzymes, ImmobilizedEsterificationGreen Chemistry TechnologyKineticsLipaseEnzymes, ImmobilizedLipaseesterificationheterogeneous catalysismembrane reactorpervaporationprocess intensification

Identifiers

PMID40806342
PMCPMC12347235

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.