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Zhang et al. Soft Sci 2024;4:39 https://dx.doi.org/10.20517/ss.2024.34 Page 23 of 28

Long-term stability
Long-term stability is of great significance for hydrogel electrodes in brain signal monitoring. This stability
requires adequate mechanical, electrical and chemical properties. However, due to their high water content,
hydrogels are susceptible to dehydration or swelling in difference humidity conditions, which can
compromise the stability and quality of the signal. Potential solutions include designing complex network
structures by incorporating an independent cross-linking network within the existing hydrogel matrix, to
enhance mechanical strength and durability. Additionally, the use of solvent exchange methods or multi-
solvent systems to create a stable oil-water mixture can help resist dehydration and swelling effects.
Furthermore, embedding flexible polymers or microfibers within the hydrogel to form a multi-scale porous
structure can improve fatigue resistance under prolonged mechanical stress, reducing structural damage
caused by expansion.

Multi-channel integration
As neuroscience progresses, multi-channel neural electrodes have become crucial tools for investigating the
mechanisms of brain activity and its spatial distribution. Multi-channel integration allows for high-density,
precise acquisition of brain signals, which can provide researchers with detailed patterns of neural activity
and insights into complex brain networks. However, utilizing hydrogels for multi-channel integration poses
significant challenges, particularly in achieving high channel density and signal quality while ensuring
flexibility and biocompatibility. Although some studies have embedded conductive channels within
hydrogels to create preliminary multi-channel electrodes, they often do not meet the high-density
requirements for practical applications. Possible solutions include employing micro/nanofabrication
techniques compatible with hydrogels, such as laser etching and soft lithography, to construct high-density
microelectrode arrays within hydrogels. These methods ensure precise channel distribution and minimize
inter-channel interference. Additionally, electrochemical deposition can be used to coat conductive
materials onto microelectrode surfaces, which enhances the overall conductivity of the array. In addition,
creating multi-layered hydrogel structures, where each layer contains an independent conductive network,
allows for channel isolation and reduces signal crosstalk. This layered design can further increase channel
density. Besides, a modular design for hydrogel electrodes, with each module incorporating an independent
multi-channel electrode array, allows for flexible arrangement and combination to meet specific monitoring
needs across different brain regions.

Organogels
Organogels are emerging as a promising alternative to hydrogels, particularly in applications where the
inherent limitations of hydrogels present challenges. Unlike hydrogels, which are often constrained by water
solubility and swelling behavior, organogels can provide enhanced stability and reduced leaching of active
compounds, thereby improving the efficacy and longevity of formulations. Moreover, they offer tunable
properties through the selection of different gelators and solvents, which allows for precise control over
their rheological and thermal characteristics. This versatility can address specific application requirements,
such as bioactivity and biocompatibility, which are crucial in pharmaceutical and biomedical contexts.
Additionally, organogels can mitigate issues related to microbial contamination and degradation, which are
common challenges hydrogels face in certain settings.

Integration of hydrogel interfaces with machine learning for neural decoding
Machine learning algorithms excel in extracting patterns and features from complex datasets, which is
essential for decoding neural signals obtained through hydrogel-based electrodes. By integrating hydrogel
electrodes to record high-quality signals with machine learning, researchers can significantly enhance the
accuracy and efficiency of neural signal interpretation. In addition, machine learning models can adapt to
changes in neural activity over time, which provides a dynamic decoding capability. It can evolve with the