Shortwave Infrared Organic Photodiodes Realized by Polaron Engineering.

A novel approach for developing shortwave IR (SWIR) organic photodiodes (OPDs) using doped polymers is presented. SWIR OPDs are challenging to produce because of the limitations in extending the absorption of conjugated molecules and the high dark currents of SWIR-absorbing materials. Herein, it is shown that the conversion of bound polarons to free polarons by light energy can be utilized as an SWIR photodetection mechanism. To maximize the bound-polaron density and bound-to-free polaron ratio of the doped polymer film, the doping process is engineered and dopant molecules are diffused into the crystalline domain of the polymer matrix and a direct correlation between the bound-to-free polaron ratio and device performance is confirmed. The optimized double-doped SWIR OPD exhibits a high external quantum efficiency of 77 100% and specific detectivity of 1.11 × 1011 Jones against SWIR. These findings demonstrate the application potential of polarons as alternatives for Frenkel excitons in SWIR OPDs.

Liquid-Film Rupture for Web-like Ag Nanowires toward High-Performance Organic Schottky Barrier Transistors.

Organic vertical transistors are promising device with benefits such as high operation speed, high saturation current density, and low-voltage operation owing to their short channel length. However, a short channel length leads to a high off-current, which is undesirable because it affects the on-off ratio and power consumption. This study presents a breakthrough in the development of high-performance organic Schottky barrier transistors (OSBTs) with a low off-current by utilizing a near-ideal source electrode with a web-like Ag nanowire (AgNW) morphology. This is achieved by employing a humidity- and surface-tension-mediated liquid-film rupture technique, which facilitates the formation of well-connected AgNW networks with large pores between them. Therefore, the gate electric field is effectively transmitted to the semiconductor layer. Also, the minimized surface area of the AgNWs causes complete suppression of the off-current and induces ideal saturation of the OSBT output characteristics. p- and n-type OSBTs exhibit off-currents in the picoampere range with on/off ratios exceeding 106 and 105, respectively. Furthermore, complementary inverters are prepared using an aryl azide cross-linker for patterning, with a gain of >16. This study represents a significant milestone in the development of high-performance organic vertical transistors and verifies their applicability in organic electronic circuitry.

Emerging Roles of Using Small Extracellular Vesicles as an Anti-Cancer Drug.

Small extracellular vesicles (sEVs) are emerging as a novel therapeutic strategy for cancer therapy. Tumor-cell-derived sEVs contain biomolecules that can be utilized for cancer diagnosis. sEVs can directly exert tumor-killing effects or modulate the tumor microenvironment, leading to anti-cancer effects. In this review, the application of sEVs as a diagnostic tool, drug delivery system, and active pharmaceutical ingredient for cancer therapy will be highlighted. The therapeutic efficacies of sEVs will be compared to conventional immune checkpoint inhibitors. Additionally, this review will provide strategies for sEV engineering to enhance the therapeutic efficacies of sEVs. As a bench-to-bedside application, we will discuss approaches to encourage good-manufacturing-practice-compliant industrial-scale manufacturing and purification of sEVs.

Molecular-Switch-Embedded Solution-Processed Semiconductors.

Recent improvements in the performance of solution-processed semiconductor materials and optoelectronic devices have shifted research interest to the diversification/advancement of their functionality. Embedding a molecular switch capable of transition between two or more metastable isomers by light stimuli is one of the most straightforward and widely accepted methods to potentially realize the multifunctionality of optoelectronic devices. A molecular switch embedded in a semiconductor can effectively control various parameters such as trap-level, dielectric constant, electrical resistance, charge mobility, and charge polarity, which can be utilized in photoprogrammable devices including transistors, memory, and diodes. This review classifies the mechanism of each optoelectronic transition driven by molecular switches regardless of the type of semiconductor material or molecular switch or device. In addition, the basic characteristics of molecular switches and the persisting technical/scientific issues corresponding to each mechanism are discussed to help researchers. Finally, interesting yet infrequently reported applications of molecular switches and their mechanisms are also described.

A regioregular donor-acceptor copolymer allowing a high gain-bandwidth product to be obtained in photomultiplication-type organic photodiodes.

Obtaining a photomultiplication-type organic photodiode with a high gain-bandwidth product is challenging. We show that a newly designed regioregular polymer enables the formation of a highly oriented face-on structure with a low trap density, leading to a high EQE and a fast response time. As a result, a gain-bandwidth product of over 4 × 105 Hz is achieved.

Interfacial Electrostatic-Interaction-Enhanced Photomultiplication for Ultrahigh External Quantum Efficiency of Organic Photodiodes.

A photomultiplication-type organic photodiode (PM-OPD), where an electric double layer (EDL) is strategically embedded, is demonstrated, with an exceptionally high external quantum efficiency (EQE) of 2 210 000%, responsivity of 11 200 A W-1 , specific detectivity of 2.11 × 1014 Jones, and gain-bandwidth product of 1.92 × 107  Hz, as well as high reproducibility. A polymer electrolyte, poly(9,9-bis(3'-(N,N-dimethyl)-N-ethylammoinium-propyl-2,7-fluorene)-alt-2,7-(9,9-dioctylfluorene))dibromide is employed as a work-function-modifying layer of indium tin oxide (ITO) to construct an EDL-embedded Schottky junction with p-type polymer semiconductor, poly(3-hexylthiophene-diyl), resulting in not only advantageous tuning of the work function of ITO but also an enhancement of the electron-trapping efficiency due to electrostatic interaction between exposed cations and trapped electrons within isolated acceptor domains. The effects of the EDL on the energetics of the trapped electron states and thus on the gain generation mechanism are confirmed by numerical simulations based on the drift-diffusion approximation of charge carriers. The feasibility of the fabricated high-EQE PM-OPD especially for weak light detection is demonstrated via a pixelated prototype image sensor. It is believed that this new OPD platform opens up the possibility for the ultrahigh-sensitivity organic image sensors, while maintaining the advantageous properties of organics.

BioBERT: a pre-trained biomedical language representation model for biomedical text mining.

MOTIVATION: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. RESULTS: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. AVAILABILITY AND IMPLEMENTATION: We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

CollaboNet: collaboration of deep neural networks for biomedical named entity recognition.

BACKGROUND: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. RESULTS: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. CONCLUSIONS: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.

Giant Brunner's Gland Adenoma of the Proximal Jejunum Presenting as Iron Deficiency Anemia and Mimicking Intussusceptions.

Brunner's gland adenoma is a rare benign proliferative lesion developing most commonly in the posterior wall of the duodenum. It is usually small in size and asymptomatic. Depending on its size or location, however, the clinical manifestations of this tumor may be variable from nonspecific symptoms to gastrointestinal bleeding or obstruction. Brunner's gland adenoma in the proximal jejunum is extremely rare. We report a very rare case of giant Brunner's gland adenoma developing in the proximal jejunum which presented as iron deficiency anemia and mimicked intussusceptions on radiologic studies.