Inkjet Printed Metal–Organic Frameworks for Non‐Volatile Memory Devices Suitable for Printed RRAM

Abstract Inkjet printing has emerged as a promising technique for patterning functional materials, offering significant advantages over traditional subtractive thin‐film methods. Its versatility enables the structuring of various materials, expanding application ranges and minimizing waste through additive manufacturing. However, the limited availability of functional material‐based inks suitable for inkjet printing presents challenges in ink formulation. HKUST‐1, a 3D cubic metal–organic frameworks (MOFs) comprised of copper(II) ions coordinated to benzene‐1,3,5‐tricarboxylate (BTC) organic linkers, known for its porosity and tunability, have potential to enhance inkjet‐printed devices. This study combines inkjet printing and evaporation‐induced crystallization to structure HKUST‐1, marking the first demonstration of nanocrystalline HKUST‐1 integrated into a printed electronic device, specifically a memristor, where the MOF is prepared by inkjet printing of a precursor solution. Memristors, which change their resistance based on the external stimuli history, enabling the construction of resistive random‐access memory (RRAM). The fabricated memristors in this study exhibit notable properties: low forming voltage, an R off /R on ratio of 10 4 , a retention time of 600 s, and endurance exceeding 60 write and erase cycles. This research highlights the potential of integrating MOFs into inkjet printing, unlocking broader application possibilities, and advancing additive manufacturing for functional materials.

Authors

Institutions

Publication Details

Journal
Advanced Functional Materials
Published
2024-12-16
DOI
https://doi.org/10.1002/adfm.202412372
Citations
21
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
4.38

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Inkjet Printed Metal–Organic Frameworks for Non‐Volatile Memory Devices Suitable for Printed RRAM

Jasmin Aghassi‐Hagmann, Matthias Schwotzer, Hartmut Gliemann, Franz Fischer et al.
21 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
4.38
article

Inkjet Printed Metal–Organic Frameworks for Non‐Volatile Memory Devices Suitable for Printed RRAM

Jasmin Aghassi‐Hagmann, Matthias Schwotzer, Hartmut Gliemann, Franz Fischer, Christian Rainer, Christof Wöll, Hongrong Hu, Uli Lemmer, Ben Breitung, Carsten Natzeck, Yan Liu
article en
21 citations

Abstract

Abstract Inkjet printing has emerged as a promising technique for patterning functional materials, offering significant advantages over traditional subtractive thin‐film methods. Its versatility enables the structuring of various materials, expanding application ranges and minimizing waste through additive manufacturing. However, the limited availability of functional material‐based inks suitable for inkjet printing presents challenges in ink formulation. HKUST‐1, a 3D cubic metal–organic frameworks (MOFs) comprised of copper(II) ions coordinated to benzene‐1,3,5‐tricarboxylate (BTC) organic linkers, known for its porosity and tunability, have potential to enhance inkjet‐printed devices. This study combines inkjet printing and evaporation‐induced crystallization to structure HKUST‐1, marking the first demonstration of nanocrystalline HKUST‐1 integrated into a printed electronic device, specifically a memristor, where the MOF is prepared by inkjet printing of a precursor solution. Memristors, which change their resistance based on the external stimuli history, enabling the construction of resistive random‐access memory (RRAM). The fabricated memristors in this study exhibit notable properties: low forming voltage, an R off /R on ratio of 10 4 , a retention time of 600 s, and endurance exceeding 60 write and erase cycles. This research highlights the potential of integrating MOFs into inkjet printing, unlocking broader application possibilities, and advancing additive manufacturing for functional materials.

Advanced Functional MaterialsVol. 35(4)
Karlsruhe Institute of Technology (DE)
Deutsche Forschungsgemeinschaft
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
Advanced Memory and Neural Computing
4.38
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.