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The chemistry of DNA endows it with certain functional properties that facilitate the generation of self-assembled nanostructures, offering precise control over their geometry and morphology, that can be exploited for advanced biological applications. Despite the structural promise of these materials, their applications are limited owing to lack of functional capability to interact favourably with biological systems, which has been achieved by functional proteins or peptides. Herein, we outline a strategy for functionalizing DNA structures with short-peptides, leading to the formation of DNA-peptide hybrid materials. This proposition offers the opportunity to leverage the unique advantages of each of these bio-molecules, that have far reaching emergent properties in terms of better cellular interactions and uptake, better stability in biological media, an acceptable and programmable immune response and high bioactive molecule loading capacities. We discuss the synthetic strategies for the formation of these materials, namely, solid-phase functionalization and solution-coupling functionalization. We then proceed to highlight selected biological applications of these materials in the domains of cell instructionmolecular recognition, gene delivery, drug delivery and bonetissue regeneration. We conclude with discussions shedding light on the challenges that these materials pose and offer our insights on future directions of peptide-DNA research for targeted biomedical applications.

The doping strategy of ferrite nanoparticles induced a correlation between their reactivity and toxicity. The evidence showed the induction of biological responses as a factor of their dissolution and suspension properties of ferrite nanoparticles.

Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.

Owing to highly favourable properties such as enormous internal surface areas, high porosity and large flexibility, when it comes to the choice of precursors and high control over their structures and porosity, metal-organic frameworks (MOFs) have emerged as promising materials for applications such as gas storage and separation, catalysis, wastewater filtration

HKUST-1 Metal-Organic Framework (MOF), due to its coordinatively unsaturated metal sites, high surface area and microporosity, is one the most prominent MOF candidates. Our study investigates the consequence of doping iron into HKUST-1 MOF on its hydrostability, and demonstrates its usability for Pb(II) removal. A simple one-pot solvothermal process was used to synthesize Fe doped HKUST-1 MOF with varying dopant concentrations of Fe (5�20 mol). Through various characterisation techniques (XPS, XRD, FTIR, ICP-OES) and MD simulations we demonstrated the incorporation of Fe into the HKUST-1 framework, which happened through Fe substituting the Cu(II) sites. Water stability studies using experimental and modelling approaches demonstrated that partial substituted Fe-HKUST-1 MOFs have a greater surface area retention with very little loss in crystallinity as compared to pristine HKUST-1 MOF. Fe substitution of as low as 5 mol yielded a significant enhancement in the hydrostability of HKUST-1 MOFs, wherein the structure was intact for up to 10 hrs of water treatment. Similarly, 5 mol Fe doping had a 53 reduction in the amount of Cu being leached out from the HKUST-1 MOF framework. Fe doped HKUST-1 MOFs showed exceptionally high Pb(II) selectivity, Pb(II) removal efficiency of > 90, and a high Pb(II) adsorption capacity of 565 mg g�1. This study opens up the possibility of using doping as a strategy to enhance the hydrostability of HKUST-1 MOF and in the process improve its applicability for environmental remediation applications. © 2021 Elsevier B.V.