Precise controlled filling of point vacancies in hBN with carbon atoms is demonstrated using a focused electron beam method, which guides mobile C atoms into the desired defect site. Optimization of ...the technique enables the insertion of a single C atom into a selected monovacancy, and preferential defect filling with sub‐2 nm accuracy. Increasing the C insertion process leads to thicker 3D C nanodots seeded at the hBN point vacancy site. Other light elements are also observed to bind to hBN vacancies, including O, opening up a wide range of complex defect structures that include B, C, N, and O atoms. The ability to selectively fill point vacancies in hBN with C atoms provides a pathway for creating non‐hydrogenated covalently bonded C molecules embedded in the insulating hBN.
Site selective filling of vacancy sites in hBN monolayers with carbon atoms is achieved using a focused electron probe as a guide. Carbon atoms are fully bonded in the hBN with minimal strain. Spatial control of this process was demonstrated down to 5 nm precision, opening a way forward to stable embedded Carbon molecules in an insulating hBN host.
Abstract
Single-photon emitters are crucial building blocks for optical quantum technologies. Hexagonal boron nitride (hBN) is a promising two-dimensional material that hosts bright, room-temperature ...single-photon emitters. However, photo instability is a persistent challenge preventing practical applications of these properties. Here, we reveal the ubiquitous photobleaching of hBN vacancy emitters. Independent of the source or the number of hBN layers, we find that the photobleaching of a common emission at 1.98 ± 0.05 eV can be described by two consistent time constants, namely a first bleaching lifetime of 5 to 10 s, and a second bleaching lifetime in the range of 150 to 220 s. Only the former is environmentally sensitive and can be significantly mitigated by shielding O
2
, whereas the latter could be the result of carbon-assisted defect migration. Annular dark-field scanning transmission electron microscopy of photobleached hBN allows for visualizing vacancy defects and carbon substitution at single atom resolution, supporting the migration mechanism along with X-ray photoelectron spectroscopy. Thermal annealing at 850 °C of liquid exfoliated hBN eliminates both bleaching processes, leading to persistent photostability. These results represent a significant advance to potentially engineer hBN vacancy emitters with the photostability requisite for quantum applications.
The first global workshop on implementation of the WHO guidelines on procedures and data requirements for changes to approved biotherapeutic products adopted by the WHO Expert Committee in 2018 was ...held in June 2019. The workshop participants recognized that the principles based on sound science and the potential for risk, as described in the WHO Guidelines on post-approval changes, which constitute the global standard for product life-cycle management are providing clarity and helping national regulatory authorities in establishing guidance while improving time-lines for an efficient regulation of products. Consequently, the regulatory situation for post-approval changes and guideline implementation is changing but there is a disparity between different countries. While the guidelines are gradually being implemented in some countries and also being considered in other countries, the need for regional workshops and further training on post-approval changes was a common theme reiterated by many participants. Given the complexities relating to post-approval changes in different regions/countries, there was a clear understanding among all participants that an efficient approach for product life-cycle management at a national level is needed to ensure faster availability of high standard, safe and efficacious medicines to patients as per the World Health Assembly Resolution 67.21.
•WHO Guidelines constitute the global standard for product life-cycle management.•The Guidelines provide principles based on sound science and potential for risk.•The Guidelines help regulatory authorities in establishing national guidance.•Efficient approach for product life-cycle management at a national level is needed.
We consider the Markov Decision Process (MDP) of selecting a subset of items at each step, termed the Select-MDP (S-MDP). The large state and action spaces of S-MDPs make them intractable to solve ...with typical reinforcement learning (RL) algorithms especially when the number of items is huge. In this paper, we present a deep RL algorithm to solve this issue by adopting the following key ideas. First, we convert the original S-MDP into an Iterative Select-MDP (IS-MDP), which is equivalent to the S-MDP in terms of optimal actions. IS-MDP decomposes a joint action of selecting K items simultaneously into K iterative selections resulting in the decrease of actions at the expense of an exponential increase of states. Second, we overcome this state space explo-sion by exploiting a special symmetry in IS-MDPs with novel weight shared Q-networks, which prov-ably maintain sufficient expressive power. Various experiments demonstrate that our approach works well even when the item space is large and that it scales to environments with item spaces different from those used in training.
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