Unidirectional scattering with spatial homogeneity using photonic time disorder

The temporal degree of freedom in photonics has been a recent research hotspot due to its analogy with spatial axes, causality, and open-system characteristics. In particular, the temporal analogues of photonic crystals have stimulated the design of momentum gaps and their extension to topological and non-Hermitian photonics. Although recent studies have also revealed the effect of broken discrete time-translational symmetry in view of the temporal analogy of spatial Anderson localization, the broad intermediate regime between time order and time uncorrelated disorder has not been examined. Here we investigate the inverse design of photonic time disorder to achieve optical functionalities in spatially homogeneous platforms. By developing the structure factor and order metric using causal Green's functions for the domain of time disorder, we demonstrate engineered time scatterer, which provides unidirectional scattering with controlled scattering amplitudes. We also reveal that the order-to-disorder transition in the time domain allows for the manipulation of scattering bandwidths, which inspires resonance-free temporal colour filtering. Our work will pave the way for advancing optical functionalities without spatial patterning.Comment: 22 pages, 4 figure

Transformation of (allo)securinine to (allo)norsecurinine via a molecular editing strategy

Securinega alkaloids have intrigued chemists since the isolation of securinine in 1956. This family of natural products comprises a securinane subfamily with a piperidine substructure and norsecurinane alkaloids featuring a pyrrolidine core. From a biosynthetic perspective, the piperidine moiety in securinane alkaloids derives from lysine, whereas the pyrrolidine moiety in norsecurinane natural products originates from ornithine, marking an early biogenetic divergence. Herein, we introduce a single-atom deletion strategy that enables the late-stage conversion of securinane to norsecurinane alkaloids. Notably, for the first time, this method enabled the transformation of piperidine-based (allo)securinine into pyrrolidine-based (allo)norsecurinine. Straightforward access to norsecurinine from securinine, which can be readily extracted from the plant Flueggea suffruticosa, abundant across the Korean peninsula, holds promise for synthetic studies of norsecurinine-based oligomeric securinega alkaloids

Chiral symmetry and taste symmetry from the eigenvalue spectrum of staggered Dirac operators

We investigate general properties of the eigenvalue spectrum for improved staggered quarks. We introduce a new chirality operator $[\gamma_5 \otimes 1]$ and a new shift operator $[1 \otimes \xi_5]$, which respect the same recursion relation as the $\gamma_5$ operator in the continuum. Then we show that matrix elements of the chirality operator sandwiched between two eigenstates of the staggered Dirac operator are related to those of the shift operator by the Ward identity of the conserved $U(1)_A$ symmetry of staggered fermion actions. We perform a numerical study in quenched QCD using HYP staggered quarks to demonstrate the Ward identity. We introduce a new concept of leakage patterns which collectively represent the matrix elements of the chirality operator and the shift operator sandwiched between two eigenstates of the staggered Dirac operator. The leakage pattern provides a new method to identify zero modes and non-zero modes in the Dirac eigenvalue spectrum. This method is as robust as the spectral flow method but requires much less computing power. Analysis using a machine learning technique confirms that the leakage pattern is universal, since the staggered Dirac eigenmodes on normal gauge configurations respect it. In addition, the leakage pattern can be used to determine a ratio of renormalization factors as a by-product. We conclude that it might be possible and realistic to measure the topological charge $Q$ using the Atiya-Singer index theorem and the leakage pattern of the chirality operator in the staggered fermion formalism.Comment: 27 pages, 78 figures, 10 tables, references updated, more explanation adde

MicroNano2008-70266 FABRICATION OF PHASE REFLECTION SAWTOOTH GRATINGS OPTIMIZED BY SCALAR AND VECTOR WAY BY USING DIAMOND CUTTING

ABSTRACT This paper presents optimization of phase reflection sawtooth gratings with a period of 2.0 ㎛ and a depth of 0.2 ㎛ based on the Fourier transformation (FT) and the rigorous coupled wave analysis (RCWA). And its fabrication on oxygen free Cu and electroless Ni-coated surfaces by using diamond cutting in a shaping process whose toolpath is interfered to provide smaller period. The diffraction efficiencies were estimated 100% for FT, 83.0% and 79.0% for TE and TM polarization of the incident light at a depth of 0.2 ㎛ It was found that electroless Ni-coated surface had better performance in terms of machining and optical functionality. From optical testing, the diffraction efficiencies were measured 84.0% and 84.4% for TE and TM polarization, respectively

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts.</p

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome

Highly Active Heterogeneous Catalysts for Carbon Dioxide Reduction to Value-Added Chemicals

Carbon dioxide (CO2) utilization is indispensable to reduce high atmospheric CO2 concentration, attributing to global warming and ocean acidification. Reduction of CO2 into high value-added chemicals and fuels is a promising process to mitigate CO2 emissions and opens possibilities to have carbon-based energy production with net-zero carbon emissions. CO2 is the most oxidized form of carbon and thermodynamically stable. To effectively reduce CO2, nanotubular yolk–shell catalysts for methane reforming, and tandem catalysts for direct CO2 hydrogenation to light olefins were developed. In the former process, Ni yolks encapsulated with SiO2 shell demonstrated excellent stability with a high resistance to carbon deposition in the confined morphology due to the efficient CO desorption. Forming Pt–Ni single-atom alloys on the yolks pushed the catalyst operating temperature down to 500 °C and further improved the catalyst stability due to the enhanced Ni reducibility. In the latter process, indium oxide supported on zirconia and SAPO-34 zeolite were operated as a tandem catalyst to produce a high light olefins selectivity by shifting the reaction equilibrium to the right for the CO2 to methanol conversion. Zirconia promoted with yttria (YSZ) inhibited the reduction and hydroxylation of active indium sites. The improved oxygen vacancy formation in YSZ and strong metal–support interaction between indium oxide and YSZ resulted in stable light olefins production. These discoveries can be adopted to the current power generation and manufacturing processes to utilize CO2 emissions to produce high value-added chemicals with net-zero carbon emissions