Functional Janus structured liquids and aerogels.

Janus structures have unique properties due to their distinct functionalities on opposing faces, but have yet to be realized with flowing liquids. We demonstrate such Janus liquids with a customizable distribution of nanoparticles (NPs) throughout their structures by joining two aqueous streams of NP dispersions in an apolar liquid. Using this anisotropic integration platform, different magnetic, conductive, or non-responsive NPs can be spatially confined to opposite sides of the original interface using magnetic graphene oxide (mGO)/GO, TiCT/GO, or GO suspensions.

Alignment of Breathing Metal-Organic Framework Particles for Enhanced Water-Driven Actuation.

As the majority of known metal-organic frameworks (MOFs) possess anisotropic crystal lattices and thus anisotropic physicochemical properties, a pressing practical challenge in MOF research is the establishment of robust and simple processing methods to fully harness the anisotropic properties of the MOFs in various applications. We address this challenge by applying an E-field to precisely align MIL-88A microcrystals and generate MIL-88A@polymer films. Thereafter, we demonstrate the impact of MOF crystal alignment on the actuation properties of the films as a proof of concept.

Reticular Nanoscience: Bottom-Up Assembly Nanotechnology.

The chemistry of metal-organic and covalent organic frameworks (MOFs and COFs) is perhaps the most diverse and inclusive among the chemical sciences, and yet it can be radically expanded by blending it with nanotechnology. The result is reticular nanoscience, an area of reticular chemistry that has an immense potential in virtually any technological field. In this perspective, we explore the extension of such an interdisciplinary reach by surveying the explored and unexplored possibilities that framework nanoparticles can offer.

Metallodrugs in cancer nanomedicine.

Metal complexes are extensively used for cancer therapy. The multiple variables available for tuning (metal, ligand, and metal-ligand interaction) offer unique opportunities for drug design, and have led to a vast portfolio of metallodrugs that can display a higher diversity of functions and mechanisms of action with respect to pure organic structures. Clinically approved metallodrugs, such as cisplatin, carboplatin and oxaliplatin, are used to treat many types of cancer and play prominent roles in combination regimens, including with immunotherapy.

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning.

Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures.

The Current Status of MOF and COF Applications.

The amalgamation of different disciplines is at the heart of reticular chemistry and has broadened the boundaries of chemistry by opening up an infinite space of chemical composition, structure, and material properties. Reticular design has enabled the precise prediction of crystalline framework structures, tunability of chemical composition, incorporation of various functionalities onto the framework backbone, and as a consequence, fine-tuning of metal-organic framework (MOF) and covalent organic framework (COF) properties beyond that of any other material class. Leveraging the unique properties of reticular materials has resulted in significant advances from both a fundamental and an applied perspective.

25 Years of Reticular Chemistry.

At its core, reticular chemistry has translated the precision and expertise of organic and inorganic synthesis to the solid state. While initial excitement over metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) was undoubtedly fueled by their unprecedented porosity and surface areas, the most profound scientific innovation of the field has been the elaboration of design strategies for the synthesis of extended crystalline solids through strong directional bonds. In this contribution we highlight the different classes of reticular materials that have been developed, how these frameworks can be functionalized, and how complexity can be introduced into their backbones.

Artificial Bioaugmentation of Biomacromolecules and Living Organisms for Biomedical Applications.

The synergistic union of nanomaterials with biomaterials has revolutionized synthetic chemistry, enabling the creation of nanomaterial-based biohybrids with distinct properties for biomedical applications. This class of materials has drawn significant scientific interest from the perspective of functional extension controllable coupling of synthetic and biomaterial components, resulting in enhancement of the chemical, physical, and biological properties of the obtained biohybrids. In this review, we highlight the forefront materials for the combination with biomacromolecules and living organisms and their advantageous properties as well as recent advances in the rational design and synthesis of artificial biohybrids.

Machine Learning for Tailoring Optoelectronic Properties of Single-Walled Carbon Nanotube Films.

A machine learning technique, namely, support vector regression, is implemented to enhance single-walled carbon nanotube (SWCNT) thin-film performance for transparent and conducting applications. We collected a comprehensive data set describing the influence of synthesis parameters (temperature and CO concentration) on the equivalent sheet resistance (at 90% transmittance in the visible light range) for SWCNT films obtained by a semi-industrial aerosol (floating-catalyst) CVD with CO as a carbon source and ferrocene as a catalyst precursor. The predictive model trained on the data set shows principal applicability of the method for refining synthesis conditions toward the advanced optoelectronic performance of multiparameter processes such as nanotube growth.