Evidence map›Paper›PMID 41561449›Full record

ArticleChemical science2026

Decoding polyethylene formation in Cr/PNP catalyzed ethylene oligomerization

Youcai Zhu, Yue Mu, Xiaoke Shi, Long Chen, Shu Yang, Li Sun, Zhen Liu

Abstract read
In one paragraph

Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Youcai ZhuSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.ORCID https://orcid.org/0000-0002-7925-9650
Yue MuSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.
Xiaoke ShiSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.
Long ChenSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.
Shu YangSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.
Li SunSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.ORCID https://orcid.org/0000-0002-5249-4052
Zhen LiuSchool of Chemical Engineering, East China University of Science and Technology Shanghai 200237 China liuzhen@ecust.edu.cn.ORCID https://orcid.org/0000-0003-3103-4011

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polyethylene formation remains a critical side reaction in ethylene selective oligomerization, lowering α-olefin yields and lacking reliable predictive strategies. Here, an automated workflow incorporating structural parsing and indexing was developed to construct a Cr/PNP catalysts library, extract comprehensive molecular descriptors, and integrate them with machine learning models trained on experimentally measured PE values. A combined classification-regression strategy enabled accurate identification of low-PE catalysts and quantitative prediction of PE contents, establishing a broadly applicable framework for catalyst design and side-product control in homogeneous catalysis. Analysis of SHAP feature importance and DFT calculations revealed that the intrinsic properties of the ligand primarily determine whether it produces low or high PE. In contrast, optimizing experimental conditions plays a pivotal role in further reducing PE in low-PE ligands.

Identifiers

PMID41561449
PMCPMC12814957

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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.