Evidence map›Paper›PMID 40871849›Full record

ReviewSensors (Basel, Switzerland)2025

3D Printing in the Design of Potentiometric Sensors: A Review of Techniques, Materials, and Applications.

Aleksandra Zalewska, Nikola Lenar, Beata Paczosa-Bator

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 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

3 authors.

Aleksandra ZalewskaFaculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30, PL-30059 Krakow, Poland.ORCID 0009-0004-6651-4669
Nikola LenarFaculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30, PL-30059 Krakow, Poland.ORCID 0000-0002-7482-6767
Beata Paczosa-BatorFaculty of Materials Science and Ceramics, AGH University of Krakow, Mickiewicza 30, PL-30059 Krakow, Poland.ORCID 0000-0002-2838-462X

Funding

The publication is financed from the subsidy no 16.16.160.557 of the Polish Ministry of Science and Education 16.16.160.557
6 · The paper itself

Abstract

The integration of 3D printing into the development of potentiometric sensors has revolutionized sensor fabrication by enabling customizable, low-cost, and rapid prototyping of analytical devices. Techniques like fused deposition modeling (FDM) and stereolithography (SLA) allow researchers to produce different sensor parts, such as electrode housings, solid contacts, reference electrodes, and even microfluidic systems. This review explains the basic principles of potentiometric sensors and shows how 3D printing helps solve problems faced in traditional sensor manufacturing. Benefits include smaller size, flexible shapes, the use of different materials in one print, and quick production of working prototypes. However, some challenges still exist-like differences between prints, limited chemical resistance of some materials, and the long-term stability of sensors in real-world conditions. This paper overviews recent examples of 3D-printed ion-selective electrodes and related components and discusses new ideas to improve their performance. It also points to future directions, such as better materials and combining different manufacturing methods. Overall, 3D printing is a powerful and growing tool for developing the next generation of potentiometric sensors for use in healthcare, environmental monitoring, and industry.

Indexed as

3D printingelectrochemical sensorsion-selective electrodesrapid prototypingsensor miniaturization

Identifiers

PMID40871849
PMCPMC12390291

What OpenQuestion holds

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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.