Surface-normal illuminated pseudo-planar Ge-on-Si avalanche photodiodes with high gain and low noise.

Germanium-on-Silicon (Ge-on-Si) avalanche photodiodes (APDs) are of considerable interest as low intensity light detectors for emerging applications. The Ge absorption layer detects light at wavelengths up to ≈ 1600 nm with the Si acting as an avalanche medium, providing high gain with low excess avalanche noise. Such APDs are typically used in waveguide configurations as growing a sufficiently thick Ge absorbing layer is challenging. Here, we report on a new vertically illuminated pseudo-planar Ge-on-Si APD design utilizing a 2 µm thick Ge absorber and a 1.4 µm thick Si multiplication region. At a wavelength of 1550 nm, 50 µm diameter devices show a responsivity of 0.41 A/W at unity gain, a maximum avalanche gain of 101 and an excess noise factor of 3.1 at a gain of 20. This excess noise factor represents a record low noise for all configurations of Ge-on-Si APDs. These APDs can be inexpensively manufactured and have potential integration in silicon photonic platforms allowing use in a variety of applications requiring high-sensitivity detectors at wavelengths around 1550 nm.

Very low excess noise Al0.75Ga0.25As0.56Sb0.44 avalanche photodiode.

AlxGa1-xAsySb1-y grown lattice-matched to InP has attracted significant research interest as a material for low noise, high sensitivity avalanche photodiodes (APDs) due to its very dissimilar electron and hole ionization coefficients, especially at low electric fields. All work reported to date has been on Al concentrations of x = 0.85 or higher. This work demonstrates that much lower excess noise (F = 2.4) at a very high multiplication of 90 can be obtained in thick Al0.75Ga0.25As0.56Sb0.44 grown on InP substrates. This is the lowest excess noise that has been reported in any III-V APD operating at room temperature. The impact ionization coefficients for both electrons and holes are determined over a wide electric field range (up to 650 kV/cm) from avalanche multiplication measurements undertaken on complementary p-i-n and n-i-p diode structures. While these ionization coefficients can fit the experimental multiplication over three orders of magnitude, the measured excess noise is significantly lower than that expected from the ß/α ratio and the conventional local McIntyre noise theory. These results are of importance not just for the design of APDs but other high field devices, such as transistors using this material.

Palladium-Catalyzed Multicomponent Assembly of (Z)-Alkenylborons via Carbopalladation/Boronation/Retro-Diels-Alder Cascade Reaction.

A palladium-catalyzed multicomponent cascade reaction of aryl iodides, oxanorbornadiene, and diborns to access (Z)-alkenylborons is reported. This transformation proceeds through the sequential carbopalladation/boronation/retro-Diels-Alder domino reaction. The oxanorbornadiene used in this reaction serves as an acetylene surrogate, which is generated via a retro-Diels-Alder reaction. Such a stereoselective and scalable approach has a wide range of functional group tolerance and good substrate universality.

Transition-Metal-, Additive-, and Solvent-Free [3 + 3] Annulation of RCF2-Imidoyl Sulfoxonium Ylides with Cyclopropenones to Give Multifunctionalized CF3-Pyridones.

An efficient and practical strategy was developed to synthesize 1,3,4-triaryl-6-trifluoromethylpyridones from CF3-imidoyl sulfoxonium ylides and cyclopropenones in good to excellent yields. This stepwise [3 + 3] annulation reaction was carried out under transition-metal-, additive-, and solvent-free conditions, generating 1 equiv of dimethyl sulfoxide as byproduct and tolerating a series of functional groups.

Base-Promoted Stereoselective Hydrogenation of Ynamides with Sulfonyl Hydrazide to Give Z-Enamides.

A base-mediated semihydrogenation of ynamides using p-toluenesulfonyl hydrazide as an inexpensive and easy-to-handle hydrogen donor is reported. This transition-metal-free protocol avoids overhydrogenation and reduction of other functional groups, generating the thermodynamically unfavorable Z-enamides exclusively.

1.83-µm high-power and high-energy light source based on 885-nm in-band diode-pumped Nd:YAG bulk laser operating on 4F3/2→4I15/2 transition.

We report on 1.8-µm laser generation based on a 885-nm diode laser in-band pumping of conventional Nd:YAG bulk crystal. The maximum output power reaches 2.72 W at 1834 nm with slope efficiency of about 12.1% with respect to the absorbed power. With a Cr:ZnSe saturable absorber, passively Q-switched operation is also demonstrated with maximum average output power of 1.25 W. The achieved shortest pulse width, maximum pulse energy and peak power are 54 ns, 125.9 µJ and 2.27 kW, respectively. The results are very competitive to many reported Tm3+ lasers at 1.9 µm. However, this 1834-nm wavelength is indeed difficult to generate from Tm3+ solid-state lasers, which bridges the wavelength gap for potential applications.

Direct generation of orthogonally polarized dual-wavelength continuous-wave and passively Q-switched vortex beam in diode-pumped Pr:YLF lasers.

We report on direct generation of an orthogonally polarized dual-wavelength vortex laser, for the first time to our knowledge, by means of a diode-pumped V-shaped Pr:YLF laser platform. A method of misaligning the folded mirror is proposed to realize the simultaneous orthogonally polarized dual-wavelength laser, while a method of orthogonally rotating the laser gain medium is proposed to generate an intracavity vortex beam (LG01 mode). With the two methods, in continuous-wave (CW) mode, we have achieved simultaneous lasing of an orthogonally polarized dual-wavelength vortex laser at 604 and 607 nm with maximum output power of 237.7 mW. Moreover, based on this operation, a simultaneous orthogonally polarized dual-wavelength passively Q-switched vortex laser is also realized by inserting a Co:ASL crystal into the laser resonator as a saturable absorber. This work provides the simplest way for direct generation of an orthogonally polarized dual-wavelength vortex laser for potential applications.

DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning.

INTRODUCTION: Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositioning for new drug development. In the recent decade, machine learning methods have been applied to predict the binding affinity of compound proteins, while deep learning is recently becoming prominent and achieving significant performances. Among the models, the way of representing the compounds is usually simple, which is the molecular fingerprints, i.e., a single SMILES string. METHODS: In this study, we improve previous work by proposing a novel representing manner, named SMILES#, to recode the SMILES string. This approach takes into account the properties of compounds and achieves superior performance. After that, we propose a deep learning model that combines recurrent neural networks with a convolutional neural network with an attention mechanism, using unlabeled data and labeled data to jointly encode molecules and predict binding affinity. RESULTS: Experimental results show that SMILES# with compound properties can effectively improve the accuracy of the model and reduce the RMS error on most data sets. CONCLUSION: We used the method to verify the related and unrelated compounds with the same target, and the experimental results show the effectiveness of the method.

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. RESULT: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Annulation of CF3-Imidoyl Sulfoxonium Ylides with 1,3-Dicarbonyl Compounds: Access to 1,2,3-Trisubstituted 5-Trifluoromethylpyrroles.

A lithium-bromide-promoted nucleophilic substitution/annulation cascade reaction between CF3-imidoyl sulfoxonium ylides and 1,3-dicarbonyl compounds has been established, and the corresponding 1,2,3-trisubstituted 5-trifluoromethylpyrroles have been obtained in 27-78% yield. This reaction features a broad substrate scope and generates dimethyl sulfoxide and H2O as byproducts.

Transition Metal-Free C-H Thiolation via Sulfonium Salts Using ß-Sulfinylesters as the Sulfur Source.

We disclose a direct C(sp)-, C(sp2)-, and C(sp3)-H thiolation reaction using ß-sulfinylesters as the versatile sulfur source. The key step of this protocol is chemoselective C-S bond cleavage of the sulfonium salts that are formed in situ from the corresponding alkenes, alkynes, and 1,3-dicarboxyl compounds with ß-sulfinylesters. The successful capture of the acrylate byproduct supports a retro-Michael reaction mechanism.

Rhodium(II)-catalyzed multicomponent assembly of α,α,α-trisubstituted esters via formal insertion of O-C(sp3)-C(sp2) into C-C bonds.

The direct cleavage of C(CO)-C single bonds, delivering otherwise inaccessible compounds, is a significant challenge. Although the transition metal-catalyzed insertion of functional groups into C(CO)-C bonds has been studied, strained ketone substrates or chelating assistance were commonly required. In this article, we describe a rhodium(II)-catalyzed three-component reaction of 1,3-diones, diazoesters, and N,N-dimethylformamide (DMF), leading to an unusual formal insertion of O-C(sp3)-C(sp2) into unstrained C(CO)-C bonds. This procedure provides a rapid entry to a gamut of otherwise inaccessible α,α,α-trisubstituted esters/amide from relatively simple substrates in a straightforward manner. 55 examples of highly decorated products demonstrate the broad functional group tolerance and substrate scope. The combination of control experiments and isotope-labeling reactions support that O, C(sp3), and C(sp2) units derive from 1,3-diones, diazoesters, and DMF, respectively.