GONG, et al
Superadhesive Hydrogel
https://www.youtube.com/shorts/3AMvXrdAy1A
Japanese scientists developed a superglue that works underwater
Researchers at Hokkaido University developed ultra-strong underwater adhesives by combining insights from nature with artificial intelligence.
The team analyzed over 24,000 adhesive proteins from marine organisms like mussels and barnacles, identifying common molecular patterns that enable wet adhesion.
They synthesized 180 hydrogels based on these patterns, then used machine learning to optimize the formulations.
After three AI-guided rounds of predictions and testing, they achieved adhesive strength exceeding one megapascal—ten times stronger than previous underwater glues.
The material instantly sealed high-pressure water leaks, withstood ocean waves for over a year, and outperformed commercial...
https://www.global.hokudai.ac.jp/news/23001/
Getting sticky: the highest-performing underwater adhesive hydrogel polymer
Inspired by biology, researchers have achieved the highest performing underwater adhesive hydrogel technology to date through a data mining and machine learning approach.
Hydrogels are a permeable soft material consisting of polymer networks and water with applications ranging from bio-medical engineering to contact lenses. Intrinsic to hydrogels is the ability to endow diverse characteristics by modifying their polymer networks. Professor Gong’s research lab at WPI-ICReDD, Hokkaido University, specializes in hydrogel technology and has engineered hydrogels with self-strengthening, self-healing, underwater adhesion properties and more. For adhesive hydrogels, achieving instant, strong, and repeatable underwater adhesion is a prevailing challenge.
Through a combination of data mining and machine learning, Professor Gong, Professor Takigawa, Professor Fan, graduate student Liao, and colleagues have recently developed the strongest underwater-adhesive hydrogels to date with adhesive strengths (Fa) exceeding 1 MPa. The gels’ strength was both instant and repeatable and they are functional across various surfaces under variable levels of salinity from pure water to seawater. This research was published in Nature and was selected for the cover.
For reference, if these hydrogels were cut to the size of a single postage stamp (2.5 x 2.5 cm), they could theoretically support ~63 kg (e.g. an adult human). The researchers demonstrated the hydrogel’s adhesive strength by applying it to a rubber duck on a seaside rock where it withstood repeated ocean tides and wave impacts.
A rubber duck attached to a seaside rock using the hydrogel as a glue withstood repeated ocean tides and wave impacts, demonstrating its adhesive strength.
Taking inspiration from biology, these hydrogels were designed with polymer networks derived from adhesive proteins found in archaea, bacteria, eukaryotes, and viruses. Despite the diversity across these organisms, these proteins share common sequence patterns that endow adhesion in wet environments. For this, ~25,000 adhesive protein datasets, collected from the National Center for Biotechnology Information (NCBI) protein database, were data mined for relevant amino acid sequences important for underwater adhesion.
They replicated these sequences into polymer networks and synthesized 180 hydrogels—each containing unique polymer networks. The data compiled from studying these hydrogels were analyzed with machine learning which further extrapolated the most significant polymer sequences. The original 180 gels synthesized from data mining demonstrated adhesive qualities greater than gels previously reported in the literature. However, the gels inspired by machine learning were more incredible, exceeding the highly desired qualities mentioned above.
Repeatable and instant adhesion are highly desired qualities for applications ranging from biomedical engineering and deep-sea exploration. These qualities are confirmed in an experiment in which the water leak from a damaged pipe could be covered instantly and repeatedly.
The significance of the data driven approach in this research is clearly highlighted upon comparison of these hydrogels with previous conventional models. Such a distinct advancement in overall performance should lead to exciting new discoveries and applications for adhesive hydrogel applications.
https://www.nature.com/articles/s41586-025-09269-4
DOI: 10.1038/s41586-025-09269-4
Nature volume 644, pages 89–95 (2025)
Data-driven de novo design of super-adhesive hydrogels
Hongguang Liao, Sheng Hu, Hu Yang, Lei Wang, Shinya Tanaka, Ichigaku Takigawa, Wei Li, Hailong
Abstract -- Data-driven methodologies have transformed the discovery and prediction of hard materials with well-defined atomic structures by leveraging standardized datasets, enabling accurate property predictions and facilitating efficient exploration of design spaces1,2,3. However, their application to soft materials remains challenging because of complex, multiscale structure–property relationships4,5,6. Here we present a data-driven approach that integrates data mining, experimentation and machine learning to design high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we developed a descriptor strategy to statistically replicate protein sequence patterns in polymer strands by ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations from an initial dataset of 180 bioinspired hydrogels, achieving remarkable improvements in adhesive strength, with a maximum value exceeding 1?MPa. These super-adhesive hydrogels hold immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a notable advancement in data-driven innovation for soft materials.
https://cen.acs.org/materials/biobased-materials/AI-designed-superglue-retains-extreme/103/web/2025/08
Nature 2025, DOI: 10.1038/s41586-025-09269-4
Extremely sticky, waterproof glues have long been a critical necessity of mankind. Now, researchers have developed two such superglues that were designed by artificial intelligence after taking inspiration from a plethora of sticky proteins found in nature
One of the holy grails of materials science has been concocting strong and reliable waterproof glues, a quest that’s notoriously dependent upon trial and error, thus making the process luck based. Traditional glues often fail in wet environments because water disrupts the critical interactions needed for adhesion. Yet, nature is replete with organisms such as mussels, barnacles, and other marine creatures that have evolved to adhere strongly in wet, even turbulent conditions.
Hailong Fan of Hokkaido University, Japan, and his team mined a comprehensive dataset of over 24,000 adhesive proteins from bacteria, eukaryotes, archaea, and viruses, spanning over 3,800 species. “Rather than mimicking one organism like the mussel, we essentially let evolution be our guide, treating nature as a massive design database,” Fan says.
They found that despite their taxonomical diversity, these proteins shared characteristic amino acid sequences, especially the pairwise arrangements of amino acid functional classes involved in adhesion. Next, they created 180 novel, waterproof glues from random, free-radical copolymerization of six monomers, each representative of an amino acid functional class.
Then the team measured the underwater strength of every glue, with an Escherichia derived glue emerging strongest at 147 kilopascal. Mussels, by comparison, can latch onto rocks with roughly 800 kPa of force. The researchers used this data to train machine learning models to conjure novel, better performing designs and predict their underwater strengths. Next, the team synthesized glues predicted to have topnotch strengths, measured their actual strengths, and then again fed this data to the ML models. Ultimately, they ended up with three sample glues (named R1-max, R2-max and R3-max), each being the top-performer of its respective “learning” round.
They found that the glues exhibit mind-boggling underwater strength with R1-max topping the chart at more than 1 million Pa. More than 200 cycles of attachment and detachment failed to weaken R1’s grip, and it held together plates of various materials under a 1 kg load for more than a year, demonstrating its reusability and longevity. A rubber duck that was attached to a seaside rock with R1-max withstood relentless crashes of ocean waves and tides, a testament to its exceptional durability. And R2-max instantly sealed a 2-cm-diameter hole at the base of a three-meter pipe filled with tap water.
HYDROGEL, METHOD FOR PRODUCING HYDROGEL, AND METHOD FOR PRODUCING MOLDED ARTICLE -- WO2022249904 // Translation // US11492433
Provided is a hydrogel which has an interpenetrating network structure or a semi-interpenetrating network structure, and which contains a first polymer having a three-dimensional network structure and a second polymer introduced into gaps of the three-dimensional network structure of the first polymer, wherein the first polymer contains, as monomeric unit, a cross-linkable monomer A1 having a bond with a bonding energy of 80 to 250 kJ/mol, the crosslinking degree of the second polymer is smaller than the crosslinking degree of the first polymer, and the content of the second polymer is 2-100 parts by mass relative to 1 part by mass of the first polymer.
ULTRA HIGH-STRENGTH GEL HAVING BIOCOMPATIBILITY -- WO2004110513
A hydrogel having a mutual invasion network structure or a hydrogel having a semi-mutual invasion network structure which is excellent in biocompatibility and mechanical strength, characterized in that a charged natural polymer is provided in a bacteria cellulose network.
TEMPERATURE-RESPONSIVE GEL HAVING LCST WITH NO VOLUME PHASE TRANSITION, AND PRODUCTION METHOD THEREFOR -- US2020087485
COMPOSITE COMPRISING FABRIC AND POLYAMPHOLYTE HYDROGEL AND PREPARATION METHOD THEREOF -- US2017258571
Method for Bone-filling Cartilage Tissue for Inducing Regeneration of the Cartilage -- US2011257763
BONE-FILLING TYPE AGENT FOR INDUCING CARTILAGE REGENERATION -- US2011159099
MATERIAL FOR CELL ADHESION AND PROLIFERATION -- WO2004112860