National Chemical Laboratory

We report the synthesis of homochiral crystalline covalent organic framework (COF) films that combine rigidity and porosity, offering significant promise for heterogeneous asymmetric catalysis. We prepared enantiopure COF films from achiral diamine and trialdehyde precursors using a chiral induction crystallization strategy. A Schiff-base reaction, catalyzed by a chiral acid, namely (R)- and (S)-camphorsulfonic acids (CSA) generated a β-ketoenamine backbone with induced chirality. (R)- and (S)-camphorsulfonic acids direct the diamine-trialdehyde condensation and induce chirality during the nucleation of COF nanotubes, enabling their periodic arrangement and the formation of homochiral thin films. We successfully synthesized six distinct COF films with three different backbone functionalities: R-, S-TpAzo; R-, S-TpDPP; and R-, S-TpBDMe2, using the two enantiomers of the chiral camphorsulfonic acids. All films displayed strong circular dichroism signals and pronounced Cotton effects, confirming their enhanced enantiopurity. Both R- and S-TpAzo films exhibited the highest crystallinity, long-range order, and permanent porosity, making them particularly well-suited for catalytic applications. To demonstrate their utility, we encapsulated a bioinspired catalyst, (Et4N)2[FeIII(Cl)bTAML], within the chiral channels of the R- and S-TpAzo films. The resulting composite created a robust heterogeneous catalytic platform for the asymmetric epoxidation of alkenes, achieving excellent activity and enantioselectivity under ambient conditions.

ABSTRACT A new method is reported for the synthesis of β ‐carbolines and their derivatives using H 5 IO 6 in DMSO solvent. The method is mild and efficient for oxidative decarboxylation and aromatization of tetrahydro β ‐carbolines (TH β C), resulting in good to excellent yields (70% to 88%) of β ‐carbolines. This protocol has further been utilized for the synthesis of β ‐carbolines based natural products, for examples, norharmane, harmane, harmine, Kumujian‐C, and Orthoscuticellines A and B.

Conversion of agro-waste to value-added products is one of the important principles of a green circular economy. A novel sustainable technique has been reported by using chlorine-free extraction of cellulose nanofibers (CNFs) from the peels of Salacca zalacca, a common fruit found in Asia. The fruit peels were exposed to alkali treatment using 2% NaOH (60 °C, 2 h), chlorine-free bleaching using 15% hydrogen peroxide (60 °C, 2 h), and sulfuric acid hydrolysis (15% v/v) for the extraction of CNFs. Various analytical methods were employed to characterize the prepared CNFs. The Fourier transform infrared spectroscopy (FTIR) and 13C solid state nuclear magnetic resonance (NMR) spectra showed the complete elimination of lignin and hemicellulose in the prepared CNFs. The elemental composition and high purity of CNFs were further verified by energy-dispersive X-ray analysis (EDX). The scanning electron microscopy (SEM) and transmission electron microscopy (TEM) images proved the fibrous morphology of the prepared CNFs, and the diameters of the cellulose nanofibers were found to be 15-30 nm. The X-ray diffraction (XRD) studies disclosed the type-I cellulosic structure in the prepared CNFs with a high crystallinity index (73%). The thermogravimetric analysis (TGA) demonstrated the superior thermal stability of the prepared CNFs (T max is 350 °C) compared to the raw fiber (T max is 320 °C). Based on the obtained results, it has been explored that the extracted highly pure CNFs can be used for fabricating bionanocomposites for high performance applications, including food packaging, sensors, water treatment, green tires, etc.

In recent years, symmetry-breaking has emerged as a powerful tool for significantly altering various physical properties in 2D layered materials. However, the breaking of symmetry by means of mechanical stress in organic crystals remains elusive. Here, we demonstrate a simple approach to engineer symmetry-breaking through mechanical stress fields in a layered molecular crystal, resulting in autonomous and fast self-healing under ambient temperature and pressure conditions. Fracture mechanics analysis reveals that the crystal adheres to an elasto-plastic model, with formation of a plastic zone at the crack tip, which prevents further crack propagation, facilitating the self-healing process. Spatially resolved Raman mapping reveals that the crack formation is accompanied by a distinct symmetry-breaking mechanism at the microstructural level. A six-fold increase in non-linear second harmonic (SH) activity, triggered by mechanical perturbation, further validates the local symmetry breaking in an otherwise centrosymmetric crystal. Furthermore, symmetry is restored following successful healing, as evidenced by the disappearance of the SH signal in the healed regions. This study not only broadens the scope of self-healing mechanisms viable in molecular materials but also offers key insights into the role of symmetry breaking and its potential for related technological applications.

Progress in solid-state hydrogen storage is critically constrained by the time consuming experimental measurements. While machine learning offers a promising route to accelerate materials screening, its accuracy is fundamentally limited by the scarcity of quality experimental data. To overcome this bottleneck, we introduce an adaptive learning (AL) framework that integrates uncertainty quantification into a closed-loop workflow for targeted material optimization. Unlike static models, this framework actively guides the selection of high-value compositions to maximize information gain and improve model predictability. As a proof of concept, we apply the framework for the prediction of pressure-composition-temperature (PCT) isotherms of Mg-Ni-La compositions using literature-reported data. Starting with a baseline model trained solely on PCT isotherms of Mg-Ni compositions, the model is iteratively refined through ten adaptive learning cycles. The uncertainty-driven composition selection effectively 1 identifies informative data points spanning Mg fractions from 92% to 4% and temperatures from 300 K to 633 K, demonstrating strong exploration capability across the chemical space. For ten unseen Mg-Ni-La compositions evaluated sequentially, the model accuracy converges upto 80% within the first five cycles, with predictions aligning well with experimental observations. This work demonstrates adaptive learning as a robust, data efficient predictive framework for accelerating the screening and optimization of hydrogen storage materials while minimizing experimental efforts.

Cimetidine, a popular histamine H2-receptor antagonist, represents a complex structural landscape exhibiting multiple forms. Attempts to synthesize a bicomponent salt with fumaric acid suffered from crystallization challenges in the past, especially toward the growth of anhydrous single crystals. In this work, we address these crystallization challenges by adopting an alternative crystallization approach involving ionic liquids and analyzing the structural landscape with a large synthon-based approach. Two novel forms of cimetidine fumarate were isolated. The structural differences in the forms of cimetidine fumarate were further explored by using different coformers as structural probes.