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Record W4413434541 · doi:10.1021/acs.accounts.5c00472

Overcoming the Hydration and Solvation Problem in Ion Recognition and Binding: The Biomimetic Approach

2025· article· en· W4413434541 on OpenAlexfundno aff
Pavel Anzenbacher, Sandra George, Austin R. Sartori, Mikhail Zamkov, Alexander N. Tarnovsky

Bibliographic record

VenueAccounts of Chemical Research · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsnot available
FundersDivision of ChemistryMcMaster University
KeywordsSolvationChemistryIonChemical physicsNanotechnologyComputational chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Artificial receptors for cations and anions utilizing noncovalent binding, transport, sequestration, or sensing in aqueous media must address enthalpic factors such as electrostatic attraction, hydrogen bonding, van der Waals forces, and London dispersion forces. The entropic component also significantly contributes to the free energy of association, especially in polar environments like water, where binding may be entropy-driven due to the release of ordered solvent molecules from the solvation sphere, increasing system entropy and resulting in a negative free Gibbs energy. Over the years, chemists have focused on enthalpic criteria, such as size complementarity and functional group interactions, for designing artificial receptors. However, designing for the entropic component, particularly solvation/desolvation, remains challenging and often depends on fortunate circumstances.As shown by X-ray crystallography, enzymes and proteins can strip solvating water molecules from the ions. Inspired by phosphate-binding enzymes and transporters, we examined polymers comprising amide bonds, such as polyamides and polyurethanes, to mimic protein backbones. These hydrophilic polymers can be engineered to absorb specific amounts of water (10-100% or even more). We aimed to use hydrophilic polymers to remove water molecules from hydrated ions, rendering them "naked" ions, thus enabling better recognition by receptors based on enthalpic factors. To test this, we used copolymers with amide and urethane-amide moieties with different ratios of poly(ethylene oxide) and poly(butylene oxide) to control water uptake between 10% and 100%, along with embedded fluorescent sensors. We found that polymers with 30-50% water uptake showed the highest fluorescence response, while uptake below 20% resulted in small changes in fluorescence and 60-100% led to diminished responses. Low water uptake caused reduced ion co-transport, while high uptake formed large water pools within the polymers, isolating solvated ions from receptors. The optimal water uptake of 30-50% produced (semi)naked ions and a water-organic matrix similar to that of DMSO-water environments. Just like proteins, the structure impacts the recognition and internalization of anions, such as phosphate or sulfate; here too, the monomer composition and synthetic sequence greatly influence material responses to anions, with lipophilic ones eliciting lower responses. The data analysis of fluorescence responses enables the generation of sensor arrays for both cations and anions in water, buffers, saliva, urine, or blood plasma, both qualitative and quantitative analyses, for single analytes or as analyte mixtures. Overall, this biomimetic approach focused on the recovery of the enthalpic factor (by diminishing the impact of solvation and entropy) has proven remarkably successful in creating sensors and adsorbents for charged species in aqueous media and water and is expected to find applications in optical sensors, sensor arrays, ion-selective electrodes, and other analytical methods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.326
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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