Tailoring ZnO nanowire morphology by substrate-position-controlled VLS growth for label-free protein sensing

ZnO nanostructures are emerging as powerful platforms for optical biosensing; however, direct control over the relationship between morphology and sensing performance remains challenging. In this study, ZnO architectures were engineered via vapor–liquid–solid (VLS) growth using substrate-position control, leading to the formation of nanowires with branched tree-like morphologies and different optical signatures. Intense defect-related photoluminescence (PL) was systematically quenched upon adsorption of thiolated bovine serum albumin (BSA-thiol). After UV activation, submicromolar detection (0.6 μM) was achieved, placing this platform among the most sensitive label-free optical biosensors reported to date. A direct correlation between growth parameters, morphology, and biosensing efficiency was established, providing a strategy for tailoring ZnO surfaces for protein detection. • VLS growth tuning substrate position yields novel tree-like ZnO nanowires. • First direct link between ZnO morphology and protein sensing performance. • Defect-related PL quenching enables label-free detection down to 0.6 μM. • UV activation enhances ZnO–protein interactions for sensitive biosensing. • Morphology-guided design paves route to next-gen optical biosensors.

Authors

Institutions

Publication Details

Journal
Materials Letters
Published
2026-01-22
DOI
https://doi.org/10.1016/j.matlet.2026.140137
Primary Topic
ZnO doping and properties
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tailoring ZnO nanowire morphology by substrate-position-controlled VLS growth for label-free protein sensing

Karina Portillo-Cortez, Amauri Serrano-Lázaro, C. Sánchez Aké, Juan C. Durán Álvarez et al.
Materials Letters
ZnO doping and properties
article

Tailoring ZnO nanowire morphology by substrate-position-controlled VLS growth for label-free protein sensing

Karina Portillo-Cortez, Amauri Serrano-Lázaro, C. Sánchez Aké, Juan C. Durán Álvarez, R. Castañeda-Guzmán, Ma. Beatriz de la Mora Mojica
article en

Abstract

ZnO nanostructures are emerging as powerful platforms for optical biosensing; however, direct control over the relationship between morphology and sensing performance remains challenging. In this study, ZnO architectures were engineered via vapor–liquid–solid (VLS) growth using substrate-position control, leading to the formation of nanowires with branched tree-like morphologies and different optical signatures. Intense defect-related photoluminescence (PL) was systematically quenched upon adsorption of thiolated bovine serum albumin (BSA-thiol). After UV activation, submicromolar detection (0.6 μM) was achieved, placing this platform among the most sensitive label-free optical biosensors reported to date. A direct correlation between growth parameters, morphology, and biosensing efficiency was established, providing a strategy for tailoring ZnO surfaces for protein detection. • VLS growth tuning substrate position yields novel tree-like ZnO nanowires. • First direct link between ZnO morphology and protein sensing performance. • Defect-related PL quenching enables label-free detection down to 0.6 μM. • UV activation enhances ZnO–protein interactions for sensitive biosensing. • Morphology-guided design paves route to next-gen optical biosensors.

Materials LettersVol. 408
Institut Català de Nanociència i Nanotecnologia (ES), Universidad Nacional Autónoma de México (MX)
Openalex Percentile: Top 81%
ZnO doping and properties
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.