Interplay of sidewall damage and light extraction efficiency of micro-LEDs.

This Letter describes the impact of shape on micro light-emitting diodes (µLEDs), analyzing 400 µm2 area µLEDs with various mesa shapes (circular, square, and stripes). Appropriate external quantum efficiency (EQE) can yield internal quantum efficiency (IQE) which decreases with increasing peripheral length of the mesas. However, light extraction efficiency (ηe) increased with increasing mesa periphery. We introduce analysis of Jpeak (the current at peak EQE) since it is proportional to the non-radiative recombination. Etching the sidewalls using tetramethylammonium hydroxide (TMAH) increased the peak EQE and decreased the sidewall dependency of Jpeak. Quantitatively, the TMAH etching reduced non-radiative surface recombination by a factor of four. Hence, shrinking µLEDs needs an understanding of the relationship between non-radiative recombination and ηe, where analyzing Jpeak can offer new insights.

Scalable graph neural network for NMR chemical shift prediction.

Graph neural networks (GNNs) have been proven effective in the fast and accurate prediction of nuclear magnetic resonance (NMR) chemical shifts of a molecule. Existing methods, despite their effectiveness, suffer from high space complexity and are therefore limited to relatively small molecules. In this work, we propose a scalable GNN for NMR chemical shift prediction. To reduce the space complexity, we sparsify the graph representation of a molecule by regarding only heavy atoms as nodes and their chemical bonds as edges. To better learn from the sparsified graph representation, we improve the message passing and readout functions in the GNN. For the message passing function, we adapt the attention mechanism and residual connection to better capture local information around each node. For the readout function, we use both node-level and graph-level embeddings as the local and global information to better predict node-level chemical shifts. Through the experimental investigation using 13C and 1H NMR datasets, we demonstrate that the proposed method yields higher prediction accuracy and is more scalable to large molecules having many heavy atoms.

Tracking Efficiency Improvement According to Incident Beam Size in QPD-Based PAT System for Common Path-Based Full-Duplex FSO Terminals.

Free space optical (FSO) communication can support various unmanned aerial vehicles' (UAVs) applications that require large capacity data transmission. In order to perform FSO communication between two terminals, it is essential to employ a pointing, acquisition, and tracking (PAT) system with an efficient and optimal performance. We report on the development of a common optical-path-based FSO communication system, tailored for applications in UAVs. The proposed system is equipped with a quadrant photodiode (QPD)-based PAT system without an additional beacon beam subsystem. The presented approach reduced the structural complexity and improved the tracking efficiency for the same size, weight, and power (SWaP). To achieve a robust FSO link in a dynamic UAV environment, the observability and controllability were obtained based on the linearized control according to the incident beam size on the QPD, which was verified by optical simulation and experiments. As a result, the QPD-based PAT system for implementing FSO links demonstrated an up to 4.25 times faster tracking performance. Moreover, the FSO link experimentally confirmed the 1.25 Gbps full-duplex error-free communication at a 50 m distance.

Valid, Plausible, and Diverse Retrosynthesis Using Tied Two-Way Transformers with Latent Variables.

Retrosynthesis is an essential task in organic chemistry for identifying the synthesis pathways of newly discovered materials, and with the recent advances in deep learning, there have been growing attempts to solve the retrosynthesis problem through transformer models, which are the state-of-the-art in neural machine translation, by converting the problem into a machine translation problem. However, the pure transformer provides unsatisfactory results that lack grammatical validity, chemical plausibility, and diversity in reactant candidates. In this study, we develop tied two-way transformers with latent modeling to solve those problems using cycle consistency checks, parameter sharing, and multinomial latent variables. Experimental results obtained using public and in-house datasets demonstrate that the proposed model improves the retrosynthesis accuracy, grammatical error, and diversity, and qualitative evaluation results verify its ability to suggest valid and plausible results.

Data undersampling models for the efficient rule-based retrosynthetic planning.

Computer-aided retrosynthetic planning for organic molecules, which is based on a large synthetic database, is a significant part of the recent development of autonomous robotic chemists. As in other AI fields, however, the class imbalance problem in the dataset affects the prediction performance of retrosynthetic paths. Here, we demonstrate that applying undersampling models to the imbalanced reaction dataset can improve the prediction of retrosynthetic templates for target molecules. We report improvements in the top-1 and top-10 prediction accuracies by 13.8% (13.1, 5.4%) and 8.8% (6.9, 2.4%) for undersampling based on the similarity (random, dissimilarity) clustering of molecular structures of products, respectively. These results demonstrate the importance of deep understanding of the statistical distribution, internal structure, and sampling for the training dataset. For practical applications, the target-oriented undersampling model is proposed and confirmed by the improved prediction performance of 9.3 and 4.2% for the top-1 and top-10 accuracies, respectively.

Neural Message Passing for NMR Chemical Shift Prediction.

Fast and accurate prediction of NMR spectra enables automatic structure validation and elucidation of molecules on a large scale. In this Article, we propose an improved method of learning from an NMR database to predict the chemical shifts of NMR-active atoms of a new molecule. For this purpose, we use a message passing neural network that operates on the graph representation of a molecule. The compactness and informativeness of the graph representation are enhanced by treating hydrogen atoms implicitly and incorporating various node and edge features. Experimental investigation demonstrates that the proposed method achieves higher prediction performance for the chemical shifts in the 1H NMR and 13C NMR spectra of small molecules. We apply this method to determine the correct molecular structure for a new NMR spectrum by searching from a set of candidate molecules.

Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations.

Recently, machine learning has been successfully applied to the prediction of nuclear magnetic resonance (NMR) chemical shifts. To build a prediction model, the existing methods require a training data set that comprises molecules whose NMR-active atoms are annotated with their chemical shifts. However, the laborious task of atomic-level annotation must be manually conducted by chemists. Thus, it becomes difficult to perform large-scale annotation. To address this issue, we propose a weakly supervised learning method to enable the predictive modeling of NMR chemical shifts without requiring explicit atomic-level annotations in the training data set. For the training data set, the proposed method only requires the annotation of chemical shifts at the molecular level. As a prediction model, we build a message passing neural network (MPNN) that predicts the chemical shifts of individual NMR-active atoms in a molecule. Using a loss function that is invariant to the permutation of atoms in a molecule, the model is trained in a weakly supervised manner to minimize the molecular-level difference between a set of predicted chemical shifts and the corresponding set of actual chemical shifts across the training data set. Accordingly, during the training, the chemical shifts predicted by the model are approximately aligned with the actual chemical shifts in a data-driven fashion. The proposed method performs comparably to the existing fully supervised methods in terms of predicting the chemical shifts of 1H and 13C NMR spectra for small molecules.

Post-operative alarm signs in the rapid response system and hospital mortality after non-cardiac surgery.

BACKGROUND: A variety of rapid response systems (RRSs) based on the systematic assessment of vital signs and laboratory tests have been developed to reduce hospital mortality through the early detection of alarm signs, while deterioration may still be reversible. This study aimed to determine the association between alarm signs and post-operative hospital mortality during post-operative days (POD) 0-3 in patients undergoing non-cardiac surgery. METHODS: This retrospective observational study used data from the registry of a single tertiary academic hospital. The study population included patients who were ≥18 years old, admitted between 1 January 2013 and 30 June 2018 for non-cardiac surgery, and subsequently transferred to the general ward. RESULTS: A total of 116 329 patients were included in the analysis. Among them, 10 099 patients (8.7%) showed positive alarm criteria and triggered the RRS in the post-operative ward during POD 0-3. In the multivariate logistic regression model, PaO2 <55 mm Hg, SpO2 <90%, and total CO2 <15 mmol/L were associated with a 3.57-, 3.46-, and 12.53-fold increase in post-operative hospital mortality, respectively. Moreover, when compared to the no alarm signs group, patients with 1, 2, 3, and ≥4 alarm signs showed a 2.79-, 2.76-, 6.54-, and 20.02-fold increase in hospital mortality, respectively. CONCLUSION: Increased post-operative hospital mortality was found to be associated with alarm signs detected by the RRS during POD 0-3. The post-operative alarm signs detected by the RRS may therefore be useful in determining high-risk patients who require medical interventions in the surgical ward.

Enhanced performance of InGaN/GaN MQW LED with strain-relaxing Ga-doped ZnO transparent conducting layer.

We report the enhanced optical and electrical properties of InGaN/GaN multiple quantum well (MQW) light-emitting diodes (LEDs) with strain-relaxing Ga-doped ZnO transparent conducting layers (TCLs). Ga-doped ZnO was epitaxially grown on p-GaN by metal-organic chemical vapor deposition. The optical output power of a LED with a 500-nm- thick-Ga-doped ZnO TCL increased by 30.9% at 100 mA, compared with that of an LED with an indium tin oxide (ITO) TCL. Raman spectroscopy measurement and the simulation of wavefunction overlap of electron and hole in MQWs revealed that the enhanced optical output power was attributed to the increased internal quantum efficiency due to the decreased compressive strain in the active region. The increase of optical output was also attributed to the increased optical transmittance of the Ga-doped ZnO TCL owing to its higher refractive index compared to that of ITO TCL. Furthermore, the forward voltage of LED with a Ga-doped ZnO TCL was lower than that of LED with an ITO TCL because of the increased carrier concentration and mobility in the Ga-doped ZnO TCL.

Magnetically enhanced luminescence of CdSe/ZnS quantum dot light-emitting diodes using circular ferromagnetic Co/Pt multilayer disks.

We investigate the effect of a magnetic field on red, green, and blue CdSe/ZnS quantum dot light-emitting diodes (QDLEDs). Circular multilayer ferromagnetic cobalt/platinum (Co/Pt) disks are deposited on a MgF2 layer covering an Al electrode, and a perpendicular magnetic field is applied to the QDs in the active layer. Carriers injected into the active layer are then trapped and efficiently recombined inside the QDs because of strong carrier localization caused by the perpendicular magnetic field. The luminescence of the QDLEDs in the multilayer increases by 33.31% at 7.5 V, 22.34% at 7.5 V, and 16.73% at 7.0 V compared with that of QDLEDs without the multilayer. The time-resolved photoluminescence of all the QDLEDs also indicates that their increased luminescence results from improved radiative recombination through the stronger carrier localization in the QDs.

Improved efficiency of InGaN/GaN light-emitting diodes with perpendicular magnetic field gradients.

The effect of magnetic fields on the optical output power of flip-chip light-emitting diodes (LEDs) with InGaN/GaN multiple quantum wells (MQWs) was investigated. Films and circular disks comprising ferromagnetic cobalt/platinum (Co/Pt) multilayers were deposited on a p-ohmic reflector to apply magnetic fields in the direction perpendicular to the MQWs of the LEDs. At an injection current of 20 mA, the ferromagnetic Co/Pt multilayer film increased the optical output power of the LED by 20% compared to an LED without a ferromagnetic Co/Pt multilayer. Furthermore, the optical output power of the LED with circular disks was 40% higher at 20 mA than the output of the LED with a film. The increase of the optical output power of the LEDs featuring ferromagnetic Co/Pt multilayers is attributed to the magnetic field gradient in the MQWs, which increases the carrier path in the MQWs. The time-resolved photoluminescence measurement indicates that the improvement of optical output power is owing to an enhanced radiative recombination rate of the carriers in the MQWs as a result of the magnetic field gradient from the ferromagnetic Co/Pt multilayer.

Fabrication of a vertically-stacked passive-matrix micro-LED array structure for a dual color display.

We report a color tunable display consisting of two passive-matrix micro-LED array chips. The device has combined vertically stacked blue and green passive-matrix LED array chips sandwiched by a transparent bonding material. We demonstrate that vertically stacked blue and green micro-pixels are independently controllable with operation of four color modes. Moreover, the color of each pixel is tunable in the entire wavelength from the blue to green region (450 nm - 540 nm) by applying pulse-width-modulation bias voltage. This study is meaningful in that a dual color micro-LED array with a vertically stacked subpixel structure is realized.

Very thin ITO/metal mesh hybrid films for a high-performance transparent conductive layer in GaN-based light-emitting diodes.

In this paper, we introduce very thin Indium tin oxide (ITO) layers (5, 10, and 15 nm) hybridized with a metal mesh to produce high-performance transparent conductive layers (TCLs) in near-ultraviolet light-emitting diodes (NUV LEDs). Using UV-vis-IR spectrometry, Hall measurement, and atomic force microscopy, we found that 10 nm was the optimal thickness for the very thin ITO layers in terms of outstanding transmittance and sheet resistance values as well as stable contact properties when hybridized with the metal mesh. The proposed layers showed a value of 4.56 Ω/□ for sheet resistance and a value of 89.1% for transmittance. Moreover, the NUV LEDs fabricated with the hybrid TCLs achieved ∼140% enhanced light output power compared to that of 150 nm thick ITO layers. Finally, to verify the practical usage of the TCLs for industrial applications, we packaged the NUV LED chips and obtained improved turn-on voltage (3.48 V) and light output power (∼116%) performance.

Color tunable monolithic InGaN/GaN LED having a multi-junction structure.

In this study, we have fabricated a blue-green color-tunable monolithic InGaN/GaN LED having a multi-junction structure with three terminals. The device has an n-p-n structure consisting of a green and a blue active region, i.e., an n-GaN / blue-MQW / p-GaN / green-MQW / n-GaN / Al2O3 structure with three terminals for independently controlling the two active regions. To realize this LED structure, a typical LED consisting of layers of n-GaN, blue MQW, and p-GaN is regrown on a conventional green LED by using a metal organic chemical vapor deposition (MOCVD) method. We explain detailed mechanisms of three operation modes which are the green, blue, and cyan mode. Moreover, we discuss optical properties of the device.

Ag nanoparticles-embedded surface plasmonic InGaN-based solar cells via scattering and localized field enhancement.

Ag nanoparticles are embedded in intentionally etched micro-circle p-GaN holes by means of a thermal agglomeration process to enhance the light absorption efficiency in InGaN/GaN multi-quantum-well (MQW) solar cells. The Ag nanoparticles are theoretically and experimentally verified to generate the plasmon light scattering and the localized field enhancement near the MQW absorption layer. The external quantum efficiency enhancement at a target wavelength region is demonstrated by matching the plasmon resonance of Ag nanoparticles, resulting in a Jsc improvement of 9.1%. Furthermore, the Ag-nanoparticle-embedded InGaN solar cell is effectively fabricated considering the carrier extraction that more than 70% of F.F. and 2.2 V of high Voc are simultaneously attained.

Size-controlled InGaN/GaN nanorod LEDs with an ITO/graphene transparent layer.

We introduce ITO on graphene as a current-spreading layer for separated InGaN/GaN nanorod LEDs for the purpose of passivation-free and high light-extraction efficiency. Transferred graphene on InGaN/GaN nanorods effectively blocks the diffusion of ITO atoms to nanorods, facilitating the production of transparent ITO/graphene contact on parallel-nanorod LEDs, without filling the air gaps, like a bridge structure. The ITO/graphene layer sufficiently spreads current in a lateral direction, resulting in uniform and reliable light emission observed from the whole area of the top surface. Using KOH treatment, we reduce series resistance and reverse leakage current in nanorod LEDs by recovering the plasma-damaged region. We also control the size of the nanorods by varying the KOH treatment time and observe strain relaxation via blueshift in electroluminescence. As a result, bridge-structured LEDs with 8 min of KOH treatment show 15 times higher light-emitting efficiency than with 2 min of KOH treatment.

Graphene interlayer for current spreading enhancement by engineering of barrier height in GaN-based light-emitting diodes.

Pristine graphene and a graphene interlayer inserted between indium tin oxide (ITO) and p-GaN have been analyzed and compared with ITO, which is a typical current spreading layer in lateral GaN LEDs. Beyond a certain current injection, the pristine graphene current spreading layer (CSL) malfunctioned due to Joule heat that originated from the high sheet resistance and low work function of the CSL. However, by combining the graphene and the ITO to improve the sheet resistance, it was found to be possible to solve the malfunctioning phenomenon. Moreover, the light output power of an LED with a graphene interlayer was stronger than that of an LED using ITO or graphene CSL. We were able to identify that the improvement originated from the enhanced current spreading by inspecting the contact and conducting the simulation.

InGaN/GaN microcolumn light-emitting diode arrays with sidewall metal contact.

In this study, we produce InGaN/GaN microcolumn LED (MC-LED) arrays having nonpolar metal sidewall contacts using a top-down method, where the metal contacts only with the sidewall of the columnar LEDs with an open top for transparency. The trapezoidal profile of the as-etched columns was altered to a rectangular profile through KOH treatment, exposing the nonpolar sidewalls. While the MC-LED with no treatment emitted no light because of the etch-damaged region, the MC-LEDs with KOH treatment exhibited much improved the electrical properties with the much higher shunt resistance due to the removal of the etch-damaged region. The optical output power was strongest for the MC-LED with a 5-min treatment indicating an almost complete removal of the damaged region.

Size-controlled InGaN/GaN nanorod array fabrication and optical characterization.

We demonstrate a cost-effective top-down approach for fabricating InGaN/GaN nanorod arrays using a wet treatment process in a KOH solution. The average diameter of the as-etched nanorods was effectively reduced from 420 nm to 180 nm. The spatial strain distribution was then investigated by measuring the high-resolution cathodoluminescence directly on top of the nanorods. The smaller nanorods showed a higher internal quantum efficiency and lower potential fluctuation, which can subsequently be exploited for high-efficiency photonic devices.

What stabilizes close arginine pairing in proteins?

Close stacking of arginine residues are often observed in protein structures despite the highly repulsive nature of the close like-charged groups. Physical factors stabilizing the close guanidinium ions of arginine side-chains have been previously studied in water and in protein-like environments, and the hydration free energy has been emphasized to be an important factor. However, how close arginine pairs are stabilized in real proteins has not been fully understood yet. In this paper, we show that arginine pairs are more frequently found in the protein interior than expected from the frequency of unpaired arginines buried inside protein through a statistical analysis of the protein structure database. We then confirm that 4 selected arginine pairs buried in the protein are indeed positively charged rather than neutralized, by molecular dynamics simulations and pKa estimation with molecular mechanics-Poisson-Boltzmann calculations. Further energy decomposition analysis shows that the hydration free energy may not be strong enough to overcome the repulsive Coulomb interaction between the positively charged arginine residues buried inside the protein. Instead, a highly polar interaction network is identified around each buried arginine pair, and the electrostatic interactions within such network are strong enough to stabilize the repulsive interaction of the buried arginine pair for the 4 selected cases. The polar interaction network is highly conserved evolutionarily in some proteins, implicating their roles in protein stabilization or biochemical function.