Evidence map›Paper›PMID 41078805›Full record

ArticleACS omega2025

An Empirical Method for Surface Energy Anisotropy Determination in High Symmetry Crystals.

Hongwei Liu, Nagarajan Valanoor, Kashinath Bogle, Xuan Cheng

Abstract read
In one paragraph

Article in ACS omega, 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

4 authors.

Hongwei LiuAustralian Centre for Microscopy and Microanalysis, The University of Sydney, Sydney, New South Wales 2006, Australia.
Nagarajan ValanoorSchool of Materials Science and Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia.ORCID https://orcid.org/0000-0003-2534-5868
Kashinath BogleSchool of Physical Sciences, Swami Ramanand Teerth Marathwada University, Nanded, Maharashtra 431606, India.ORCID https://orcid.org/0000-0001-9472-3696
Xuan ChengCommonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria 3168, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The crystal surface free energy is one of the most fundamental aspects influencing the morphology and performance of nanoscale materials across a diverse range of applications such as catalysis, drug delivery, and semiconductor technology. Despite this importance, its direct measurement, particularly for arbitrary high-index crystal facets, remains challenging due to the vast number of potential orientations and computational limitations. Here, we present a universal empirical model for estimating the surface energy of arbitrary Miller planes in high-symmetry crystals, including diamond cubic (DC), face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal close-packed (HCP) structures. The model uses stereographic projection of surface energy anisotropy and partitioning of the given plane into triangular zones defined by (low index) singular planes, enabling surface energy estimation with practical accuracy within 5% error in the whole orientation space. This approach offers significant computational efficiency while retaining sufficient precision for applications in crystal engineering. We demonstrate the model's extensive applicability even for oxide nanocrystals: the shape evolution of NiO nanocrystals under varying oxygen vacancy conditions is computed, achieving excellent agreement with experiments. The proposed framework provides a cost-effective, accessible starting point for surface energy calculations and hence serves as a bridge to more intensive computational methods, such as density functional theory (DFT) or molecular dynamics, supporting broader adoption across materials design workflows.

Identifiers

PMID41078805
PMCPMC12508961

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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.