Evidence map›Paper›PMID 42850727›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

A Biomimetic Solvatochromic Chip With Optimized Chemical Diversity for On-Site Liquid Identification.

Jintao Zhang, Hao Ouyang, Yin Cheng, Nuoye Xiong, Xunchi Ma, Xueling Li, Guangming Zhu, Liang Zhang, Jing Sun, Ranran Wang

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Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

10 authors.

Jintao Zhang *The State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.
Hao Ouyang *The State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.ORCID https://orcid.org/0009-0009-1728-6264
Yin Cheng *The State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.ORCID https://orcid.org/0000-0001-7647-3558
Nuoye Xiong *School of Computer Science and Technology, Xidian University, Xi'an, China.ORCID https://orcid.org/0009-0002-7870-8271
Xunchi MaSchool of Computer Science and Technology, Xidian University, Xi'an, China.ORCID https://orcid.org/0009-0004-3121-1414
Xueling LiThe State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.
Guangming ZhuSchool of Computer Science and Technology, Xidian University, Xi'an, China.ORCID https://orcid.org/0000-0003-3214-4095
Liang ZhangSchool of Computer Science and Technology, Xidian University, Xi'an, China.
Jing SunThe State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.ORCID https://orcid.org/0000-0002-9810-9551
Ranran WangThe State Key Laboratory of High Performance Ceramics, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai, China.ORCID https://orcid.org/0000-0001-5097-2834

Funding

Chinese Academy of Sciences Project for Young Scientists in Basic Research YSBR-109Joint Project from Austrian Research Promotion Agency and Chinese Academy of Sciences 931688/030GJHZ2025029MIJoint Project from Austrian Research Promotion Agency and Chinese Academy of Sciences ActFiber-FFG/CASNational Key Research and Development Program of China 2024YFB3814100National Natural Science Foundation of China 62301540National Natural Science Foundation of China 62471459National Natural Science Foundation of China U24A20228Natural Science Foundation of Shaanxi Province 2024JCJCQN-66Shanghai International Science and Technology Cooperation Project 23520711000Shanghai Rising Star Program 24QA2710600
6 · The paper itself

Abstract

Colorimetric sensors for instant and on-the-spot identification of organic liquids have been actively developed across diverse fields such as chemical hazard inspection, manufacturing process monitoring, and beverage safety. Multi-target sensing scenarios often necessitate large-scale sensor arrays, which inevitably increase the fabrication complexity, complicate visual interpretation, and exacerbate data redundancy. Here, we present a biomimetic photonic-gustatory chip (BiPC) comprising a polyacrylate-based solvatochromic disk (PSD) array. The elastic photonic crystal PSDs generate distinct color patterns when swelled by organic solvents. To fully unlock the sensing potential, a Hansen solubility parameter (HSP) framework was developed to assess the overall swelling cross-reactivity toward target liquids, facilitating the design of a compact PSD array with HSP-guided chemical diversity and maximized inter-solvent response distinctiveness. Using intuitive pattern recognition, an HSP-optimized four-PSD array effectively differentiates solvent types and mixing ratios, homologues and isomers, as well as alcoholic beverages. Furthermore, we developed a smartphone application that integrated image capture with deep-learning-assisted cloud computing for on-site solvent identification, achieving high accuracy (97.1% across 80 liquid classes) within 30 s. The HSP framework is generalizable to the design of compact sensor arrays for various field-deployable liquid sensing scenarios.

Indexed as

biological systemchipcloud computingfabricationnanotechnologysolvatochromism

Identifiers

PMID42850727

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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.