Herein, we describe an unprecedented cascade reaction to β‐stereogenic γ‐lactams involving Pd(II)‐catalyzed enantioselective aliphatic methylene C(sp3)−H alkenylation–aza‐Wacker cyclization through ...syn‐aminopalladation. Readily available 3,3′‐substituted BINOLs are used as chiral ligands, providing the corresponding γ‐lactams with broad scope and high enantioselectivities (up to 98 % ee).
An unprecedented cascade reaction to β‐stereogenic γ‐lactams involving Pd(II)‐catalyzed enantioselective aliphatic methylene C(sp3)−H alkenylation–aza‐Wacker cyclization through syn‐aminopalladation is reported. Readily available 3,3′‐substituted BINOLs are used as chiral ligands, providing the corresponding γ‐lactams with broad scope and high enantioselectivities (up to 98 % ee).
Each polymerase nucleotide addition cycle is associated with two primary conformational changes of the catalytic complex: the pre-chemistry active site closure and post-chemistry translocation. While ...active site closure is well interpreted by numerous crystallographic snapshots, translocation intermediates are rarely captured. Here we report three types of intermediate structures in an RNA-dependent RNA polymerase (RdRP). The first two types, captured in forward and reverse translocation events, both highlight the role of RdRP-unique motif G in restricting the RNA template movement, corresponding to the rate-limiting step in translocation. By mutating two critical residues in motif G, we obtain the third type of intermediates that may mimic the transition state of this rate-limiting step, demonstrating a previously unidentified movement of the template strand. We propose that a similar strategy may be utilized by other classes of nucleic acid polymerases to ensure templating nucleotide positioning for efficient catalysis through restricting interactions with template RNA.
Significant efforts have been made in improving the hydrogen evolution reaction (HER) catalytic activity in transition metal dichalcogenides (TMDs), which are promising nonprecious catalysts. ...However, previous attempts have exploited possible solutions to activate the inert basal plane, with little improvement. Among them, the most successful modification requires a careful manipulation of vacancy concentration and strain simultaneously. To fully realize the promise of TMD catalysts for HER in an easier and more effective way, a new means in tuning the HER catalytic activity is needed. Herein, we propose exploiting the inherent structural asymmetry in the recently synthesized family of Janus TMDs as a new means to stimulate HER activity. We report a density functional theory (DFT) study of various Janus TMD monolayers as HER catalysts, and identify the WSSe system as a promising candidate, where the basal plane can be activated without large applied tensile strains and in the absence of significant density of vacancies. We predict that it is possible to realize a strain-free Janus TMD-based catalyst that can readily provide promising intrinsic HER catalytic performance. The calculated density of states and electronic structures reveal that the introduction of in-gap states and a shift in the Fermi level in hydrogen adsorbed systems due to Janus asymmetry is the origin of enhanced HER activity. Our results should pave the way to design high-performance and easy-accessible TMD-based HER catalysts.
Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with ...novel classes. We remove the requirement of base class labels and learn generalizable embeddings via Unsupervised Meta-Learning (UML). Specifically, episodes of tasks are constructed with data augmentations from unlabeled base classes during meta-training, and we apply embedding-based classifiers to novel tasks with labeled few-shot examples during meta-test. We observe two elements play important roles in UML, i.e., the way to sample tasks and measure similarities between instances. Thus we obtain a strong baseline with two simple modifications - a sufficient sampling strategy constructing multiple tasks per episode efficiently together with a semi-normalized similarity. We then take advantage of the characteristics of tasks from two directions to get further improvements. First, synthesized confusing instances are incorporated to help extract more discriminative embeddings. Second, we utilize an additional task-specific embedding transformation as an auxiliary component during meta-training to promote the generalization ability of the pre-adapted embeddings. Experiments on few-shot learning benchmarks verify that our approaches outperform previous UML methods and achieve comparable or even better performance than its supervised variants.
We propose and numerically demonstrate an ultra-broadband graphene-based metamaterial absorber, which consists of multi-layer graphene/dielectric on the SiO
2
layer supported by a metal substrate. ...The simulated result shows that the proposed absorber can achieve a near-perfect absorption above 90% with a bandwidth of 4.8 Thz. Owing to the flexible tunability of graphene sheet, the state of the absorber can be switched from on (absorption > 90%) to off (reflection > 90%) in the frequencies range of 3–7.8 Thz by controlling the Fermi energy of graphene. Moreover, the absorber is insensitive to the incident angles. The broadband absorption can be maintained over 90% up to 50°. Importantly, the design is scalable to develop broader tunable terahertz absorbers by adding more graphene layers which may have wide applications in imaging, sensors, photodetectors, and modulators.
A cobalt‐catalyzed dual C(sp3)−H activation strategy has been developed and it provides a novel strategy for the synthesis of bicyclo4.1.0heptanes and bicyclo3.1.0hexanes. A key to the success of ...this reaction is the conformation‐induced methylene C(sp3)−H activation of the resulting cobaltabicyclo4.n.1 intermediate. In addition, the synthesis of bicyclo3.1.0hexane from pivalamide, by a triple C(sp3)−H activation, has also been demonstrated.
Double the activation: Described is a cobalt‐catalyzed dual C(sp3)−H activation strategy for the synthesis of bicyclo4.1.0heptanes and bicyclo3.1.0hexanes. A key to the success of this reaction is the conformation‐induced C(sp3)−H activation of the resulting cobaltabicyclo4.n.1 intermediate. Also, the synthesis of bicyclo3.1.0hexane from pivalamide by triple C(sp3)−H activation is demonstrated. Q=quinolin‐8‐yl.
This paper introduces a method for improving the sensitivity to NO2 gas of a p-type metal oxide semiconductor gas sensor. The gas sensor was fabricated using CuO nanowires (NWs) grown through thermal ...oxidation and decorated with ZnO nanoparticles (NPs) using a sol-gel method. The CuO gas sensor with a ZnO heterojunction exhibited better sensitivity to NO2 gas than the pristine CuO gas sensor. The heterojunction in CuO/ZnO gas sensors caused a decrease in the width of the hole accumulation layer (HAL) and an increase in the initial resistance. The possibility to influence the width of the HAL helped improve the NO2 sensing characteristics of the gas sensor. The growth morphology, atomic composition, and crystal structure of the gas sensors were analyzed using field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy, and X-ray diffraction, respectively.
Formation of granulation tissue is a fundamental phase in periodontal wound healing with subsequent maturation leading to regeneration or repair. However, persistently inflamed granulation tissue ...presents in osseous defects as a result of periodontitis and is routinely disrupted and discarded with non‐surgical and surgical therapy to facilitate wound healing or improve chances of regeneration. Histological assessment suggests that granulation tissue from periodontitis‐affected sites is effectively a chronic inflammatory tissue resulting from impaired wound healing due to persistence of bacterial dysbiotic bioflim. Nevertheless, the immunomodulatory potential and stem cell characteristics in granulation tissue have also raised speculation about the tissue's regenerative potential. This has led to the conception and recent implementation of surgical techniques which preserve granulation tissue with the intention of enhancing innate regenerative potential and improve clinical outcomes. As knowledge of fundamental cellular and molecular functions regulating periodontitis‐affected granulation tissue is still scarce, this review aimed to provide a summary of current understanding of granulation tissue in the context of periodontal wound healing. This may provide new insights into clinical practice related to the management of granulation tissue and stimulate further investigation.
Low-profile and broadband absorbers based on capacitive surfaces are proposed and implemented. First, the impedance of a dielectric substrate and capacitive surface is analyzed separately to ...characterize the surface impedance. The capacitive surfaces are therefore achieved with the combination of two ring resonators and used to design a three-fold and four-fold symmetric absorber. The resulting absorbers, with the thickness of about 0.076λ L at the lowest operating frequency, exhibit good absorption at -10 dB return loss over the fractional bandwidth of 82.5%-117.4%. For demonstration, one prototype of the proposed absorber was fabricated and measured, and good agreement is observed.
Considering the data collection and labeling cost in real-world applications, training a model with limited examples is an essential problem in machine learning, visual recognition, etc. Directly ...training a model on such few-shot learning (FSL) tasks falls into the over-fitting dilemma, which would turn to an effective task-level inductive bias as a key supervision. By treating the few-shot task as an entirety, extracting task-level pattern, and learning a
task-agnostic
model initialization, the model-agnostic meta-learning (MAML) framework enables the applications of various models on the FSL tasks. Given a training set with a few examples, MAML optimizes a model via fixed gradient descent steps from an initial point chosen beforehand. Although this general framework possesses empirically satisfactory results, its initialization neglects the task-specific characteristics and aggravates the computational burden as well. In this manuscript, we propose our AdaptiVely InitiAlized Task OptimizeR (
Aviator
) approach for few-shot learning, which incorporates task context into the determination of the model initialization. This task-specific initialization facilitates the model optimization process so that it obtains high-quality model solutions efficiently. To this end, we decouple the model and apply a set transformation over the training set to determine the initial top-layer classifier. Re-parameterization of the first-order gradient descent approximation promotes the gradient back-propagation. Experiments on synthetic and benchmark data sets validate that our
Aviator
approach achieves the state-of-the-art performance, and visualization results demonstrate the
task-adaptive
features of our proposed
Aviator
method.
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