Search NASA⌕ Search

DOE OSTI · 1595335

Ionic current in nanochannels grafted with pH-responsive polyelectrolyte brushes modeled using augmented strong stretching theory

Abstract

Here, we provide a theory to quantify the ionic current (i ion ) in nanochannels grafted with pH-responsive polyelectrolyte (PE) brushes. We consider the PE brushes to be modelled by our recently proposed augmented strong stretching theory (SST) model that improves the existing SST models by incorporating the effects of excluded volume interactions and an extended mass action law. Use of such augmented SST for this problem implies that this is the first study on computing i ion in PE-brush-grafted nanochannels accounting for the appropriate coupled con guration-electrostatic description of the PE brushes. i ion is obtained as functions of PE brush grafting density, medium pH and salt concentration (c ∞ ), and the density of polyelectrolyte chargeable sites (PECS). For large c ∞ , i ion increases linearly with c ∞ (as for such c ∞ , i ion becomes independent of the PE charge and is dominated by the bulk mobility and number density of the electrolyte ions), while iion is independent of c ∞ at small c ∞ (where the EDL electrostatics and the total number of ions in the system is dominated by the hydrogen ions). We further witness an enhancement of i ion for smaller pH and larger grafting density at low and moderate c ∞ , while there is little to no effect of the PECS density on the ionic current except for weakly grafted brushes at low c ∞ . We believe that this study will serve as a theoretical foundation for a large number of applications that are based on the brush-induced modification of the ionic current in a nanochannel.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sachar, Harnoor Singh, Sivasankar, Vishal Sankar, Etha, Sai Ankit, Chen, Guang, Das, Siddhartha. 2019-09-21. Ionic current in nanochannels grafted with pH-responsive polyelectrolyte brushes modeled using augmented strong stretching theory. https://doi.org/10.1002/elps.201900248

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗