Evidence map›Paper›PMID 40222990›Full record

ArticleScientific reports2025

A novel approach for wine anti-counterfeiting using laser-induced graphene chipless RFID tags on cork.

Antonio Lazaro, Marco Rodrigo Cujilema, Ramon Villarino, Marc Lazaro, David Girbau

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Antonio LazaroDepartment of Electronics, Electrics and Automatic Control Engineering, Rovira i Virgili University, Tarragona, 43007, Spain. antonioramon.lazaro@urv.cat.
Marco Rodrigo CujilemaDepartment of Electronics, Electrics and Automatic Control Engineering, Rovira i Virgili University, Tarragona, 43007, Spain.
Ramon Villarino *Department of Electronics, Electrics and Automatic Control Engineering, Rovira i Virgili University, Tarragona, 43007, Spain.
Marc Lazaro *Department of Electronics, Electrics and Automatic Control Engineering, Rovira i Virgili University, Tarragona, 43007, Spain.
David Girbau *Department of Electronics, Electrics and Automatic Control Engineering, Rovira i Virgili University, Tarragona, 43007, Spain.

Funding

Ministerio de Ciencia, Innovación y Universidades This research was funded by the projects PID2021-122399OB-I00 MICIU/AEI/10.13039/501100011033/FEDER, UE, and TED2021-130307B-I00 MICIU/AEI/10.13039/501100011033/ European Union NextGenerationEU/PRTR, and the grants PRE2019-089028 and PRE2022-103744.
6 · The paper itself

Abstract

This work introduces novel, non-cloneable chipless RFID tags designed for authentication, offering a solution to protect product brands, such as wine, liquor, oil, and other liquid products, from counterfeit practices. A unique spectral response of the embedded tag, created by depositing various conductive layers, can be achieved by combining its shape and sheet resistance. To accomplish this, a laser-induced graphene (LIG) layer is formed on a cork substrate (used as a graphene precursor), followed by an electroplating process to enhance conductivity. This paper presents a prototype scanner, designed to characterize the electromagnetic signature of the tags, compatible variable-sized wine bottles and cork stoppers. Preliminary results obtained with complex logo images demonstrate the feasibility of this technology.

Indexed as

AuthenticationChipless-RFIDCorkCounterfeitLaser-induced graphene (LIG)Near-fieldNon-cloneable tagRadio identification

Identifiers

PMID40222990
PMCPMC11994788

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.