Methods to functionalize arenes and heteroarenes in a site-selective manner are highly sought after for rapidly constructing value-added molecules of medicinal, agrochemical, and materials interest. One effective approach is the site-selective cross-coupling of polyhalogenated arenes bearing multiple, but identical, halogen groups. Such cross-coupling reactions have proven to be incredibly effective for site-selective functionalization. However, they also present formidable challenges due to the inherent similarities in the reactivities of the halogen substituents. In this Review, we discuss strategies for site-selective cross-couplings of polyhalogenated arenes and heteroarenes bearing identical halogens, beginning first with an overview of the reaction types that are more traditional in nature, such as electronically, sterically, and directing-group-controlled processes. Following these examples is a description of emerging strategies, which includes ligand- and additive/solvent-controlled reactions as well as photochemically initiated processes.

The field of metallosupramolecular chemistry has advanced rapidly in recent years. Much work in this area has focused on the formation of hollow self-assembled metal-organic architectures and exploration of the applications of their confined nanospaces. These discrete, soluble structures incorporate metal ions as 'glue' to link organic ligands together into polyhedra.Most of the architectures employed thus far have been highly symmetrical, as these have been the easiest to prepare. Such high-symmetry structures contain pseudospherical cavities, and so typically bind roughly spherical guests. Biomolecules and high-value synthetic compounds are rarely isotropic, highly-symmetrical species. To bind, sense, separate, and transform such substrates, new, lower-symmetry, metal-organic cages are needed. Herein we summarize recent approaches, which taken together form the first draft of a handbook for the design of higher-complexity, lower-symmetry, self-assembled metal-organic architectures.

The arrival of cell-based therapies is a revolution in medicine. However, its safe clinical application in a rational manner depends on reliable, clinically applicable methods for determining the fate and trafficking of therapeutic cells in vivo using medical imaging techniques─known as in vivo cell tracking. Radionuclide imaging using single photon emission computed tomography (SPECT) or positron emission tomography (PET) has several advantages over other imaging modalities for cell tracking because of its high sensitivity (requiring low amounts of probe per cell for imaging) and whole-body quantitative imaging capability using clinically available scanners. For cell tracking with radionuclides, ex vivo direct cell radiolabeling, that is, radiolabeling cells before their administration, is the simplest and most robust method, allowing labeling of any cell type without the need for genetic modification. This Review covers the development and application of direct cell radiolabeling probes utilizing a variety of chemical approaches: organic and inorganic/coordination (radio)chemistry, nanomaterials, and biochemistry. We describe the key early developments and the most recent advances in the field, identifying advantages and disadvantages of the different approaches and informing future development and choice of methods for clinical and preclinical application.

Comment in Chem Rev. 2022 Jun 8;122(11):10573-10574.

This review gives an authoritative, critical, and accessible overview of an emergent class of fluorescent materials termed "LG@MOF", engineered from the nanoscale confinement of luminescent guests (LG) in a metal-organic framework (MOF) host, realizing a myriad of unconventional materials with fascinating photophysical and photochemical properties. We begin by summarizing the synthetic methodologies and design guidelines for representative LG@MOF systems, where the major types of fluorescent guest encompass organic dyes, metal ions, metal complexes, metal nanoclusters, quantum dots, and hybrid perovskites. Subsequently, we discuss the methods for characterizing the resultant guest-host structures, guest loading, photophysical properties, and review local-scale techniques recently employed to elucidate guest positions. A special emphasis is paid to the pros and cons of the various methods in the context of LG@MOF. In the following section, we provide a brief tutorial on the basic guest-host phenomena, focusing on the excited state events and nanoscale confinement effects underpinning the exceptional behavior of LG@MOF systems. The review finally culminates in the most striking applications of LG@MOF materials, particularly the "turn-on" type fluorochromic chemo- and mechano-sensors, noninvasive thermometry and optical pH sensors, electroluminescence, and innovative security devices. This review offers a comprehensive coverage of general interest to the multidisciplinary materials community to stimulate frontier research in the vibrant sector of light-emitting MOF composite systems.

Electrochemical double-layer capacitors (EDLCs) are devices allowing the storage or production of electricity. They function through the adsorption of ions from an electrolyte on high-surface-area electrodes and are characterized by short charging/discharging times and long cycle-life compared to batteries. Microscopic simulations are now widely used to characterize the structural, dynamical, and adsorption properties of these devices, complementing electrochemical experiments and in situ spectroscopic analyses. In this review, we discuss the main families of simulation methods that have been developed and their application to the main family of EDLCs, which include nanoporous carbon electrodes. We focus on the adsorption of organic ions for electricity storage applications as well as aqueous systems in the context of blue energy harvesting and desalination. We finally provide perspectives for further improvement of the predictive power of simulations, in particular for future devices with complex electrode compositions.

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of a continuous polarizable medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the difficulty of describing the entire double layer forming at the electrified solid-liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread ab initio thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode's electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization.