Predicting the Compressive Properties of Carbon Foam Using Artificial Neural Networks.

Debela N Gurmu, Krzysztof Wacławiak, Hirpa G Lemu

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Carbon foam derived from co-carbonization of lignin and petroleum asphalt for high-efficiency solar evaporators.RSC advances · 2026
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

3 authors.

Debela N GurmuFaculty of Materials Engineering, Silesian University of Technology, 40-019 Katowice, Poland.ORCID 0009-0005-1986-7215

Krzysztof WacławiakFaculty of Materials Engineering, Silesian University of Technology, 40-019 Katowice, Poland.

Hirpa G LemuFaculty of Science and Technology, University of Stavanger, N-4306 Stavanger, Norway.ORCID 0000-0001-9588-4707

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article focusses on predicting the compressive properties of polyurethane-derived carbon foam using an artificial neural network (ANN) approach. To train the model, strain, pore density (20, 40, and 60 ppi), and solvents (acetone, ethanol, and methanol) were used as inputs, while compressive stress was used as output. Categorical variables like acetone, ethanol, and methanol were converted to binary form before training the ANN model by using one-hot encoding mechanism. Both inputs and outputs were normalized to prevent features with larger numerical ranges from dominating the training process. A feed-forward ANN with four hidden layers, each containing 100 neurons, was constructed. The performance of the ANN model was tested using three metrics: mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R

Indexed as

artificial neural networkcarbon foamcellular solidproperty prediction

Identifiers

PMID40508515

DOI10.3390/ma18112516

PMCPMC12155927

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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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.