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Chiral nanostructured materials for chiral discrimination and separation

  • Shubham Sharma , Bhaskar Vallamkonda , Akash Kumar Mishra , Neeraj Kumar Mishra and Vinod ORCID logo EMAIL logo
Published/Copyright: July 4, 2025

Abstract

Chiral nanostructured materials have emerged to be an influential class of high-performance materials used in areas ranging from enantioselective sensing, separation, and catalysis to optoelectronics. The materials are highly sophisticated and involve chiral metal-organic frameworks, covalent organic frameworks, carbon dots, and quantum dots etc. Owing to properties such as enantioselectivity, reproducibility, and catalytic properties, facilitating their incorporation into multifunctional systems these materials offer remarkable enantiomeic separations, environmental monitoring, and sustainable energy. In the past few years, various synthetic strategies such as chiral ligands, templating techniques, and environmentally friendly synthetic methods have been investigated to achieve improved chiral materials. Great advances have been achieved in chromatographic performance and scalability toward chiral membranes, composite materials, and MOFs- and COFs-based stationary phases. Furthermore, multi-mode platforms combining fluorescence, magnetism, and other recognition approaches show vast potential for label-free real-time enantiomeric detection. Here, we review the interdisciplinary advancement in the area of nanostructured chiral materials focusing on synthetic strategies, characterization, and enantiorecognition performance of common chiral organic compounds and drugs.


Corresponding author: Vinod, Department of Chemistry, University of Lucknow, Lucknow, 226007, India, e-mail:
Author ‘Vinod’ is also known as Vinod Kumar Vashistha in his previous publications.

Abbreviations

L-His-ZIF-67

l-Histidine-Zeolitic imidazolate framework-67

1D

One-dimensional

3D

Three-dimensional

AFM

Atomic force microscopy

aMOF

Amorphous metal-organic framework

AuNPs

Gold nanoparticles

BSA

Bovine serum albumin

BuMA

Butyl methacrylate

CC

Carboxylated cellulose

CCDs

Chiral carbon dots

CCOFs

Covalent organic frameworks

CCTF

Chiral covalent triazine framework

C-CuBDC

Chiral copper-based MOF

CD-MOF

Cyclodextrin-based metal–organic framework

CDMPC

Cellulose-tris(3,5-dimethylphenylcarbamate

CMOFs

Chiral metal-organic frameworks

CQDs

Chiral quantum dots

CSPs

Chiral stationary phases

CT

Charge transfer

CTAB

Cetyltrimethylammonium bromide

CTA-PES

Cellulose triacetate-polyethersulfone

CuLBH

[Cu(L-mal)(Bipy)]·H2O

Cu-TA

Cu-tartaric acid

DES

Hydrophilic deep eutectic solvent

d-Ile

d-isoleucine

DMF

Dimethylformamide

EDA-β-CD

Ethylenediamine-β-cyclodextrin

EDMA

Ethylene dimethacrylate

ee

Enantiomeric excess

Fe3O4@CuZnAl-LDH@MIL-100(Fe)

Magnetic layered double hydroxide/metal-organic framework

Gln

Glutamine

GO

Graphene oxide

GOx

Glucose oxidase

HPLC

High-performance liquid chromatography

HP-MDs

Hydroxypropyl-Maltodextrins

HP-MOFs

Hierarchical Porous Mofs

IP

Interfacial Polymerization

IPA

Isopropanol

l-Cys

l-Cysteine

l-Ile

l-Isoleucine

l-Phe

l-Phenylalanine

l-Tyr-COF

l-Tyrosine Functionalized COF

MDs

Maltodextrins

MONs

Microporous Organic Network

nano-LC

Nano-Liquid Chromatography

N-CD

Nitrogen-Doped Carbon Dot

PATP

P-Mercaptoaniline

PCS

Polyphenol Colloidal Spheres

PCTM

Polycarbonate Track-Etched Membranes

PDA

Polydopamine

PE

1-Phenylethanol

Phe

Phenylalanine

PL

Photoluminescence

PSf

Polysulfone

QCM

Quartz Crystal Microbalance

rGO-FSWCNT

Reduced Graphene Oxide-Functionalized Single-Walled Carbon Nanotube

RSDs

Relative Standard Deviations

SERS

Surface-Enhanced Raman Scattering

S-NE

S-1-(1-Naphthyl) Ethanol

TEM

Transmission Electron Microscopy

TNT

Titanate Nanotubes

TPB

1,3,5-Tris(4-Aminophenyl)Benzene

Trp

Tryptophan

UV–vis

Ultraviolet–Visible

XRD

X-ray Diffraction

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Vinod: Conceptualization, Supervision, Methodology, Formal analysis, Writing – Original Draft, Review & Editing. Shubham Sharma: Formal analysis, Data Interpretation. Akash Kumar Mishra: Writing – Original Draft. Neeraj Kumar Mishra: Data Acquisition, Writing – Original Draft, Formal analysis. Bhaskar Vallamkonda: Data Acquisition, Writing – Original Draft. Shubham Sharma: Formal analysis, Data Interpretation.

  4. Use of Large Language Models, AI and Machine Learning Tools: None to declare.

  5. Conflict of interest: Nothing to declare.

  6. Research funding: Not applicable.

  7. Data availability: No new data is generated.

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Received: 2025-04-28
Accepted: 2025-06-05
Published Online: 2025-07-04

© 2025 IUPAC & De Gruyter

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