Background:　Domoic　acid　(DA)　is　a　neurotoxic　compound　causing　amnesic　shellfish　poisoning,　secreted　by　red　algae　and　diatoms.　As　a　glutamate　analogue,　DA　accumulates　in　filter-feeding　marine　organisms,　posing　significant　health　risks　to　humans　upon　consumption.　Detecting　DA　in　marine　environments　remains　challenging　due　to　its　low　concentration　and　interference　from　complex　matrices.　Effective　detection　and　removal　require　materials　with　high　efficiency　and　selectivity,　which　traditional　inorganic　ionic　materials　lack　due　to　their　limited　adsorption　capacity　and　selectivity.　Ionic　covalent　organic　frameworks　(iCOFs)　expected　to　become　highly　efficient　DA　adsorbents　due　to　tunable　ionic　sites.　Results:　Thus,　a　zwitterionic　covalent　organic　nanosheet　(TGDB-iCONs)　was　synthesized　to　selectively　capture　DA.　TGDB-iCONs　was　prepared　by　one-step　Schiff-base　reaction　of　the　charged　monomer　triaminoguanidine　hydrochloride.　It　uniformly　distributed　positively　charged　guanidinium　and　negatively　charged　chloride　ions　on　the　surface,　forming　zwitterionic　binding　sites.　The　self-peeling　of　TGDB-iCONs　facilitated　the　exposure　of　active　sites　and　improved　the　adsorption　efficiency.　Several　binding　forces　were　generated　between　TGDB-iCONs　and　DA,　including　complementary　electrostatic　hydrophilic　interactions,　which　were　verified　by　density　functional　theory　(DFT)　calculation.　TGDB-iCONs　exhibited　ultra-fast　adsorption　kinetics　(7　min)　and　relatively　high　adsorption　capacity　(66.48　mg/g)　for　DA.　Furthermore,　TGDB-iCONs　exhibit　strong　salt　resistance,　which　is　attributed　to　the　charge　“shielding”　effect　of　the　zwitterionic　ions　present　in　TGDB-iCONs.　TGDB-iCONs　could　highly　selectively　enrich　DA　and　detect　trace　DA　from　marine　environment　including　seawater,　algae　and　marine　organisms　and　the　limit　of　detection　as　low　as　0.3　ng/kg.　Significance　and　novelty:　This　comprehensive　study　not　only　sheds　light　on　the　vast　potential　of　ionic　covalent　organic　frameworks　nanosheets　(iCONs)　in　supporting　early　warning,　control,　and　traceability　of　DA,　but　also　lays　a　solid　foundation　for　future　research　endeavors　aimed　at　designing　and　harnessing　the　unique　properties　of　iCONs.　©　2024　Elsevier　B.V.