Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cold Weather Concreting

Cold Weather Concreting

When freshly poured concrete is exposed to freezing temperatures before it has set, the water within the concrete can freeze. This expansion disrupts the setting process, delays chemical reactions necessary for hardening, and increases the volume of pores within the hardened concrete, which weakens its overall structure. If the concrete manages to reach an appreciable strength before it freezes, the damage can be somewhat mitigated.
To counteract the negative impacts of cold weather, ensuring...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Long-term smoking and early-onset diabetic retinopathy in young-onset type 2 diabetes.

European journal of ophthalmology·2026

Same author

Factors of crisis and culture in international and Chinese death education research: a comparative bibliometric analysis.

Frontiers in medicine·2026

Same author

Obinutuzumab plus bendamustine as first-line therapy for indolent B-cell lymphomas: a prospective multicenter open-label study.

MedScience·2026

Same author

Structural Determinants of μ-Opioid Receptor Antagonism and Respiratory Liability in Phenylfentanyl Analogues.

ACS chemical neuroscience·2026

Same author

Comparative efficacy of combined exercise and nutritional interventions for sarcopenia: A systematic review and network meta-analysis incorporating remote delivery models.

Archives of gerontology and geriatrics·2026

Same author

EEG Signal Classification with Data Augmentation for Epileptic Focus Localization and Deep Sleep Detection.

Sensors (Basel, Switzerland)·2026

Same journal

RETRACTED: Alshabanah et al. Elastic Nanofibrous Membranes for Medical and Personal Protection Applications: Manufacturing, Anti-COVID-19, and Anti-Colistin Resistant Bacteria Evaluation. <i>Polymers</i> 2021, <i>13</i>, 3987.

Polymers·2026

Same journal

Correction: Kang et al. Energy-Saving Electrospinning with a Concentric Teflon-Core Rod Spinneret to Create Medicated Nanofibers. <i>Polymers</i> 2020, <i>12</i>, 2421.

Polymers·2026

Same journal

Influence of Self-Adhesive Resin Composite Deep Marginal Elevation on the Sealing Ability of CAD/CAM Lithium Disilicate Glass-Ceramic Inlays: An In Vitro Study.

Polymers·2026

Same journal

Modulating Exciton Dynamics Through Fluorescent Side Group Incorporation in Benzodithiophene-Benzotriazole-Isoindigo Terpolymers.

Polymers·2026

Same journal

PLA/PBSA Biocomposites Reinforced with Tangerine Tree-Derived Agro-Industrial Waste for Rigid Packaging: Effect of Extraction Treatment on Morphology and Thermo-Mechanical Performance.

Polymers·2026

Same journal

Synergistic Coatings Based on Chitosan and <i>Eugenia caryophyllata</i> Essential Oil to Improve Postharvest Quality of <i>Capsicum chinense</i>.

Polymers·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 15, 2025

Optimized Sealing Process and Real-Time Monitoring of Glass-to-Metal Seal Structures

Optimized Sealing Process and Real-Time Monitoring of Glass-to-Metal Seal Structures

Published on: September 2, 2019

Low-Temperature Sealing Material Database and Optimization Prediction Based on AI and Machine Learning.

Honghao Jia¹, Zhongxu Tai², Rui Lyu²

¹Department of Information System, Saitama Institute of Technology, Fukaya 3690293, Japan.

|May 14, 2025

Summary

This summary is machine-generated.

This study used AI-generated data and machine learning to optimize low-temperature sealing materials, enhancing their performance and durability. The findings show AI data is effective for predicting material properties and guiding future development.

Keywords:

LLM-generated data data mining low-temperature sealing materials machine learning optimized design of materials

More Related Videos

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

Related Experiment Videos

Last Updated: May 15, 2025

Optimized Sealing Process and Real-Time Monitoring of Glass-to-Metal Seal Structures

Optimized Sealing Process and Real-Time Monitoring of Glass-to-Metal Seal Structures

Published on: September 2, 2019

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

Area of Science:

Materials Science
Chemical Engineering
Artificial Intelligence

Background:

Optimizing low-temperature sealing materials is crucial for enhancing performance and durability in demanding environments.
Traditional methods for material optimization can be time-consuming and resource-intensive.
The integration of artificial intelligence offers a novel approach to accelerate material discovery and performance prediction.

Purpose of the Study:

To investigate the use of AI-generated data (DeepSeek-v3 and GPT) for optimizing low-temperature sealing materials.
To apply machine learning models (XGBoost, neural networks) for 3D prediction and analysis of material properties.
To evaluate the efficacy of AI-generated data in predicting material performance and enhancing optimization strategies.

Main Methods:

Leveraging DeepSeek-v3 (DS) and GPT for generating synthetic material data.
Employing data expansion techniques to improve data quality and model robustness.
Utilizing machine learning algorithms, specifically XGBoost and neural networks, for predictive modeling.
Conducting 3D prediction and analysis of key properties relevant to low-temperature sealing materials.

Main Results:

Demonstrated the effectiveness of machine learning models in predicting key properties of low-temperature sealing materials.
Showcased the successful application of AI-generated data for material performance prediction.
Confirmed that data expansion techniques significantly enhance model accuracy and reliability.
Validated the potential of AI-driven approaches for material optimization.

Conclusions:

Machine learning, powered by AI-generated data, is a highly effective tool for optimizing low-temperature sealing materials.
AI-generated data can reliably predict material performance, reducing the need for extensive experimental testing.
This study provides valuable insights and a framework for future research in AI-assisted materials science and engineering.