Mian Zahid Hussain

Metal–organic frameworks (MOFs) are versatile precursors for nanostructured, heteroatom-doped carbons. However, understanding the spatiotemporally resolved mechanisms that govern crystalline-to-amorphous framework collapse, diffusion-mediated reorganization of transient intermediates, and the subsequent formation of graphitic carbon remains challenging. Here, we elucidate the transformation of the 2D leaf-shaped zeolitic imidazolate framework ZIF-L into Zn-dispersed N-doped carbon using a combination of in situ transmission electron microscopy (TEM) and X-ray diffraction (XRD). These measurements directly capture the sequential collapse of the layered MOF framework and the associated morphological evolution under controlled heating, decoupled from beam-induced effects during electron microscopy. Complementary ex-situ pyrolysis (700–1100 °C), together with high-resolution TEM, synchrotron X-ray pair distribution function (X-PDF) analysis, and electron energy-loss spectroscopy (EELS), reveals the diffusion of Zn-containing species, ligand decomposition, and progressive graphitization within the amorphous matrix. We identify temperature-dependent stages involving ligand breakdown, migration of Zn–Nx species, followed by Zn loss, and ultimately forming porous N-doped carbon at 1000 °C that exhibits the strongest electrochemical response within this series. These findings establish temperature-dependent structure–property relationships that enable the thermal engineering of MOF-derived ORR catalysts.

A heterogeneous Cu(0) catalyst enables efficient three-component carboboration and alkyl halide borylation under mild conditions, providing structurally diverse heteroatom-containing alkylboronates with excellent functional-group tolerance.

Despite extensive investigation of metal-organic framework (MOF) derived materials over the last 20 years, no systematic approach to predict the structural properties of the derived metal oxides is available. We present an integrated machine learning (ML) approach leveraging Smooth Overlap of Atomic Positions (SOAP) and multiple ML models, including Kernel Ridge Regression (KRR), to predict thermally derived zirconium dioxide (ZrO ₂ ) polymorph from diverse Zr-based precursors. By a systematic experimental dataset of calcination parameters, we train the ML model to quantitatively forecast material properties and the weight fraction of crystalline phases of the resulting ZrO ₂ . Experimental validation of model predictions confirms that the chemical composition of precursors and calcination parameters have a profound influence on the crystallinity of MOF-derived ZrO ₂ polymorph. Our findings demonstrate the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials.

Despite extensive investigation of metal-organic framework (MOF) derived materials over the last 20 years, no systematic approach to predict the structural properties of the derived metal oxides is available. We present an integrated machine learning (ML) approach leveraging Smooth Overlap of Atomic Positions (SOAP) and multiple ML models, including Kernel Ridge Regression (KRR), to predict thermally derived zirconium dioxide (ZrO2) polymorph from diverse Zr-based precursors. By a systematic experimental dataset of calcination parameters, we train the ML model to quantitatively forecast material properties and the weight fraction of crystalline phases of the resulting ZrO2. Experimental validation of model predictions confirms that the chemical composition of precursors and calcination parameters have a profound influence on the crystallinity of MOF-derived ZrO2 polymorph. Our findings demonstrate the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials.

Metal‐organic framework (MOF)‐derived materials are promising candidates for energy storage applications, particularly as supercapacitors, owing to their high porosity, tunable compositions, and pseudocapacitive behavior. However, performance optimization traditionally relies on resource‐intensive trial‐and‐error methods with limited insight into complex structural transitions. To address this challenge, a machine learning approach integrating Bayesian optimization (BO) to refine synthesis parameters systematically is presented. The importance of each parameter is assessed using correlation matrices, surrogate model structure, and Shapley values analysis in two models: random forest regressor, which achieves low prediction error, and extra trees regressor, which provides better generalization. This strategy efficiently explores the design space and significantly reduces experimental workload. Focusing on Mn‐MIL‐100‐derived MnO/C composites for supercapacitor applications, this approach identifies optimal conditions to increase energy storage performance while quantifying the influence of key process parameters. These findings demonstrate the potential of AI‐driven strategies to accelerate material discovery, enhance process efficiency, and advance the practical applications of MOF‐derived materials.

The pyrolytic synthesis of an ordered macro‐meso‐micro porous carbon cathode material (OM‐PC) with integration of a Co ₃ ZnC/Co catalyst is reported. It is derived from a Co‐doped ZIF‐8 framework via a templated in situ growth within the interstitial spaces of a preformed self‐assembled polystyrene monolith, followed by the template removal. The hierarchical 3D architecture facilitates Zn ² ⁺ diffusion and enhances reaction kinetics during charge–discharge processes. The integrated Co ₃ ZnC/Co catalyst significantly improves the surface affinity of the porous carbon host for polysulfide trapping and accelerates polysulfide redox conversion, leading to enhanced sulfur utilization, mitigated shuttle effects, and longer cycling stability. The fabricated aqueous Zn‐S battery with the sulfur‐loaded cathode denoted as S@Co ₃ ZnC/Co/OM‐PC delivers a synergistic high discharge capacity of ≈1685 mA h g− ¹ , which includes ≈115 mA h g ⁻¹ contributed from the I ₃ ⁻ /I ⁻ redox couple. The device shows low polarization and exhibits a minimal capacity decay of ≈0.027% per cycle over 400 cycles. It maintained a good rate performance of ≈1035 mA h g ⁻¹ at 3 A g ⁻¹ , with long cycling stability. In‐depth investigation reveals a multistep intermediate polysulfides conversion pathway in the aqueous electrolyte, which effectively avoids the sluggish solid‐solid conversion.

This work gives insight into rapid, in situ drug encapsulation in ZIF-8, its incorporation into an alginate matrix at 95 wt%, yielding MOF composites with controlled drug release, and preliminary in vitro studies on their antiproliferative activity.

Through the elaboration of standard atomic layer deposition (ALD) recipes to fit the properties of complex, porous metal-organic framework (MOF) substrates, it was possible to synthesize refined Pt-AIM@NU-1000 materials and advance the understanding of ALD in MOFs (AIM). It is generally understood that the post-synthetic functionalization of MOFs via the gas phase proves to be advantageous in comparison to its alternatives, like wet synthesis (e.g., impregnation) by an improved uniform metal dispersion or the elimination of time-consuming purification steps. These achievements can generally justify the use of the rather expensive and time-consuming ALD technique sufficiently, but simultaneously leave unfulfilled potential behind. By its nature, ALD promises the introduction of a high synthetic control which was now fully exploited to deposit Pt1-3 species in NU-1000 with exceptionally high loadings up to 28.4 wt. % Pt. This work lays the groundwork for further pushing the limits of AIM research.