ArticlePolymers2025
Low-Temperature Sealing Material Database and Optimization Prediction Based on AI and Machine Learning.
Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond.Molecules (Basel, Switzerland) · 2026Review
- Positive sentiments in early academic literature on DeepSeek: a cross-disciplinary mini review.Frontiers in artificial intelligence · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Optimization of low-temperature sealing materials is of great significance to improving low-temperature performance and durability. This study leverages DeepSeek-v3 (DS) and GPT-generated data and applies machine learning methods, including XGBoost and neural networks, to perform 3D prediction and analysis of key properties of low temperature sealing materials. Data expansion techniques were employed to enhance data quality and improve model prediction accuracy. Additionally, the study evaluates the applicability of AI-generated data in material performance prediction. The results demonstrate the effectiveness of machine learning in material optimization and provide valuable insights for future optimization strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.