AIMC Topic: Transition Temperature

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Meltome Atlas of Arabidopsis thaliana proteome: a melting temperature-based identification of heat and cold resistant proteins using in-silico approach.

BMC genomics
BACKGROUND: Plants are always exposed to a variety of stressful environments, including heat and drought stress, which severely impact the growth, development, and productivity of the plants. To overcome such challenges, plants have evolved diverse a...

Deep Learning-Enabled Real-Time Single-Shot Refocusing of Microwell Array for Digital Melting Curve Analysis.

Analytical chemistry
Digital melting curve analysis (dMCA) represents a breakthrough technology for multiplexed nucleic acid detection within limited fluorescence channels, utilizing thermal melting imaging postdigital PCR. However, conventional dMCA suffers from accurac...

Dual Embedding: A Fine-Tuned Language Model Approach for Accurate Polymer Glass Transition Temperature Prediction.

Journal of chemical information and modeling
Recent years have witnessed major advances in polymer informatics, yet accurately predicting polymer properties, such as the glass transition temperature (), remains a challenge. Language models like BERT have been leveraged to derive embeddings from...

Food processing strategies using glass transition and bio-food encapsulation for enhanced microbial management and food safety.

Food chemistry
The demand for sustainable food packaging methods has increased because of rising ecological issues and customer demand for eco-friendly products. Improving food safety and extending shelf life are essential targets for the food industry, which requi...

Developing Hybrid Machine Learning Frameworks for Polymer Property Prediction Based on Composition and Sequence Features.

Journal of chemical information and modeling
Artificial intelligence (AI) plays a significant role in advancing polymer science and engineering. Considering the critical role of the glass transition temperature () in determining the physical properties of polymers, this study systematically inv...

Data-Driven Modeling and Design of Sustainable High Tg Polymers.

International journal of molecular sciences
This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combine...

Hierarchical Graph Attention Network with Positive and Negative Attentions for Improved Interpretability: ISA-PN.

Journal of chemical information and modeling
With the advancement of deep learning (DL) methods in chemistry and materials science, the interpretability of DL models has become a critical issue in elucidating quantitative (molecular) structure-property relationships. Although attention mechanis...

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data.

Macromolecular rapid communications
Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes ...

Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning.

Journal of chemical information and modeling
Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric facto...

From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach.

ACS applied materials & interfaces
Ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) with high recovery stress but mild glass transition temperature () are highly desired for 3D/4D printing lightweight load-bearing structures and devices. However, a bottleneck is that h...