Evidence map›Paper›PMID 40012406›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2025

Advanced 3D Food Printing with Simultaneous Cooking and Generative AI Design.

Connie Kong Wai Lee, Yang Xu, Qiaoyaxiao Yuan, Yee Him Chan, Wing Yan Poon, Haosong Zhong, Siyu Chen, Mitch Guijun Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

8 authors.

Connie Kong Wai LeeCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Yang XuCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Qiaoyaxiao YuanCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Yee Him ChanCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Wing Yan PoonCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Haosong ZhongCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Siyu ChenCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.
Mitch Guijun LiCenter for Smart Manufacturing, Division of Integrative Systems and Design, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.ORCID https://orcid.org/0000-0001-6259-3209

Funding

Hong Kong Innovation and Technology Commission MHP/060/21Hong Kong Research Grants Council 25201620Hong Kong Research Grants Council C6001-22YHong Kong Research Grants Council JLFS/P-603/24
6 · The paper itself

Abstract

3D food printing is an indispensable technology for emerging food technologies. However, conventional nonconcurrent postprocessing methods limit the final food quality, including the unappealing nature of food ink modification, imperfections in retaining the desired food shape, and the risk of microbial contamination. Here, an artificial intelligence (AI)-enhanced solution is developed to achieve extrusion-based printing with simultaneous infrared heating, enabling in-line and rapid cooking of complex starch-based food. Noncontact graphene heaters as cooking sources present outstanding food quality control with microbial studies, microstructure analysis, and heat transfer simulation models. This integrative 3D food printing method with AI-enhanced food pattern generation and in-situ cooking significantly expands the applications for customized food creation. It paves the way for the broader adoption of heating-based 3D printing of functional materials.

Indexed as

Artificial IntelligenceCookingPrinting, Three-DimensionalGraphiteStarchGraphiteStarch3D food printingadditive manufacturinggenerative AIlaser‐induced graphene

Identifiers

PMID40012406
PMCPMC11962674

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.