Machine learning assists in carbon dots research
HEP Data Cooperation Journals
image:
Overview of ML-assisted CDs research
view moreCredit: HIGHER EDUCATION PRESS
Carbon dots, as an important type of luminescent nanomaterial, have been widely applied. However, the synthesis-structure-property relationship of carbon dots has long lacked a comprehensible framework, which has restricted the on-demand design and controllable preparation. In recent years, machine learning has provided a data-driven new paradigm for solving this complexity, but related research still faces challenges such as scattered data, unclear task boundaries, and insufficient model interpretability.
This review systematically examines recent key advances in machine learning for CDs research, focusing on three core tasks: property prediction, synthesis and inverse design, and mechanism analysis. Furthermore, from the perspective of CDs application systems, the review systematically evaluates the practical enabling role of machine learning in CDs-related applications such as sensing, biomedicine, optoelectronics, and information encryption, clearly distinguishing between its role as a tool for performance optimization and its function as a key means for rational design of CDs materials. Finally, a future-oriented pathway for machine learning-driven CDs research is proposed.
This work provides a forward-looking path for machine learning-driven CD research, facilitating the transition of this field from trial-and-error based on experience to a rational design. The work entitled “Machine Learning for Carbon Dots: Capabilities, Limitations, and the Path Toward Rational Design” was published in Advanced Powder Materials (Available online on 22 May 2026).
Journal
Advanced Powder Materials
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Machine Learning for Carbon Dots: Capabilities, Limitations, and the Path Toward Rational Design
Charged defects reshape grain growth in oxide ceramics
HEP Data Cooperation Journals
image:
Phase-field simulation of grain growth and skewed grain-size distribution in Fe-doped SrTiO3 oxide ceramics.
view moreCredit: HIGHER EDUCATION PRESS
Oxide ceramics such as strontium titanate and barium titanate play key roles in capacitors, actuators, sensors, memristors, and fuel-cell technologies. Their performance is strongly influenced by microscopic structural features formed during processing, particularly grain boundaries—the interfaces between neighboring crystal grains.
In Fe-doped SrTiO₃, doping introduces charged point defects, including oxygen vacancies and acceptor dopants. These defects can segregate at grain boundaries and form space-charge layers, which are difficult to observe directly during high-temperature sintering but can strongly affect grain-growth behavior and charge transport in the final ceramic.
To better understand these hidden processes, the authors developed a phase-field grain-growth model explicitly informed by defect chemistry. The model distinguishes the segregation energies and available site densities of oxygen vacancies and acceptor dopants in grain interiors and grain-boundary cores, while also accounting for the competition between defect diffusion and grain-boundary migration.
The model was benchmarked against established bicrystal descriptions, including the Mott–Schottky and Gouy–Chapman models. Simulations then revealed how defect-chemistry parameters govern the formation of space-charge layers, grain-boundary potentials, and grain-size evolution during sintering.
One important finding is that solute drag alone can drive grain growth away from the conventional log-normal behavior. The simulations produced skewed grain-size distributions even without considering grain misorientation or anisotropic grain-boundary mobility. This result provides new insight into the origin of abnormal grain growth in doped oxide ceramics and highlights the critical role of defect segregation.
The simulations also suggest that grain-boundary potentials can vary substantially throughout a polycrystalline microstructure. At later stages of grain growth, smaller grains tend to exhibit higher grain-boundary potentials and stronger blocking effects on ionic transport, whereas larger grains tend to show lower potentials. This heterogeneity may offer opportunities for microstructure design: conductivity could be enhanced by promoting current pathways that bypass blocking boundaries, while applications such as capacitors may benefit from maintaining smaller grains and preserving blocking grain boundaries.
The work, titled “A defect-chemistry-informed phase-field model of grain growth in oxide ceramics: application to Fe-doped SrTiO₃”, was published in Advanced Powder Materials (Available online on 29 April 2026).
Journal
Advanced Powder Materials
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
A defect-chemistry-informed phase-field model of grain growth in oxide ceramics: application to Fe-doped SrTiO3
No comments:
Post a Comment