Borophene, an elemental metallic Dirac material is predicted to have unprecedented mechanical and electronic character. Need of substrate and ultrahigh vacuum conditions for deposition of borophene restricts its large‐scale applications and significantly hampers the advancement of research on borophene. Herein, a facile and large‐scale synthesis of freestanding atomic sheets of borophene through a novel liquid‐phase exfoliation and the reduction of borophene oxide is demonstrated. Electron microscopy confirms the presence of β12, X3, and their intermediate phases of borophene; X‐ray photoelectron spectroscopy, and scanning tunneling microscopy, corroborated with density functional theory band structure calculations, validate the phase purity and the metallic nature. Borophene with excellent anchoring capabilities is used for sensing of light, gas, molecules, and strain. Hybrids of borophene as well as that of reduced borophene oxide with other 2D materials are synthesized, and the predicted superior performance in energy storage is explored. The specific capacity of borophene oxide is observed to be ≈4941 mAh g−1, which significantly exceeds that of existing 2D materials and their hybrids. These freestanding borophene materials and their hybrids will create a huge breakthrough in the field of 2D materials and could help to develop future generations of devices and emerging applications.

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We develop a nonadiabatic dynamics propagation scheme that allows interfacing diabatic quantum dynamics methods with commonly used adiabatic electronic structure calculations. This scheme uses adiabatic states as the quasi-diabatic (QD) states during a short-time quantum dynamics propagation. At every dynamical propagation step, these QD states are updated based on a new set of adiabatic basis. Using the partial linearized density matrix (PLDM) path-integral method as one specific example for diabatic dynamics approaches, we demonstrate the accuracy of the QD scheme with a wide range of model nonadiabatic systems as well as the on-the-fly propagations with density functional tight-binding (DFTB) calculations. This study opens the possibility to combine accurate diabatic quantum dynamics methods with adiabatic electronic structure calculations for nonadiabatic dynamics propagations.

We present the first application of machine learning on per-and polyfluoroalkyl substances (PFAS) for predicting and rationalizing carbon−fluorine (C−F) bond dissociation energies to aid in their efficient treatment and removal. Using a variety of machine learning algorithms (including Random Forest, Least Absolute Shrinkage and Selection Operator Regression, and Feed-forward Neural Networks), we were able to obtain extremely accurate predictions for C−F bond dissociation energies (with deviations less than 0.70 kcal/mol) that are within chemical accuracy of the PFAS reference data. In addition, we show that our machine learning approach is extremely efficient, requiring less than 10 min to train the data and less than a second to predict the C−F bond dissociation energy of a new compound. Most importantly, our approach only needs knowledge of the simple chemical connectivity in a PFAS structure to yield reliable resultswithout recourse to a computationally expensive quantum mechanical calculation or a threedimensional structure. Finally, we present an unsupervised machine learning algorithm that can automatically classify and rationalize chemical trends in PFAS structures that would otherwise have been difficult to humanly visualize or process manually. Collectively, these studies (1) comprise the first application of machine learning techniques for PFAS structures to predict/rationalize C−F bond dissociation energies and (2) show immense promise for assisting experimentalists in the targeted defluorination of specific bonds in PFAS structures (or other unknown environmental contaminants) of increasing complexity.

Even though transition metal dichalcogenides (TMDCs) are deemed to be novel photonic and optoelectronic 2D materials, the visible band gap being often limited to monolayer, hampers their potential in niche applications due to fabrication challenges. Uncontrollable defects and degraded functionalities at elevated temperature and under extreme environments further restrict their prospects. To address such limitations, the discovery of a new 2D material, α-PbO is reported. Micromechanical as well as sonochemical exfoliation of 2D atomic sheets of α-PbO are demonstrated and its optical behavior is investigated. Spectroscopic investigations indicate layer dependent band gaps. In particular, even multilayered PbO sheets exhibit visible band gap > 2 eV (direct) which is rare among semiconducting 2D materials. The emission lifetime of multilayer PbO atomic sheets is 7 ns (dim light) as compared to the monolayer which gives 2.5 ns lifetime and an intense light. Density functional theory calculations of layer dependent band structure of α-PbO matches well with experimental results. Experimental findings suggest that PbO atomic sheets exhibit hydrophobic nature, thermal robustness, microwave stability, anti-corrosive behaviour and acid resistance. This new low-cost, abundant and robust 2D material is expected to find many applications in the fields of electronics, optoelectronics, sensors, photocatalysis and energy storage.