Exploring Pyrazine-Based Organic Redox Couples for Enhanced Thermoelectric Performance in Wearable Energy Harvesters.

Thermoelectric generators (TEGs) based on thermogalvanic cells can convert low-temperature waste heat into electricity. Organic redox couples are well-suited for wearable devices due to their nontoxicity and the potential to enhance the ionic Seebeck coefficient through functional-group modifications.  Pyrazine-based organic redox couples with different functional groups is comparatively analyzed through cyclic voltammetry under varying temperatures. The results reveal substantial differences in entropy changes with temperature and highlight 2,5-pyrazinedicarboxylic acid dihydrate (PDCA) as the optimal candidate. How the functional groups of the pyrazine compounds impact the ionic Seebeck coefficient is examined, by calculating the electrostatic potential based on density functional theory. To evaluate the thermoelectric properties, PDCA is integrated in different concentrations into a double-network hydrogel comprising poly(vinyl alcohol) and polyacrylamide. The resulting champion device exhibits an impressive ionic Seebeck coefficient (Si) of 2.99 mV K-1, with ionic and thermal conductivities of ≈67.6 µS cm-1 and ≈0.49 W m-1 K-1, respectively. Finally, a TEG is constructed by connecting 36 pieces of 20 × 10-3 m PDCA-soaked hydrogel in series. It achieves a maximum power output of ≈0.28 µW under a temperature gradient of 28.3 °C and can power a small light-emitting diode. These findings highlight the significant potential of TEGs for wearable devices.

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning.

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing views or some uncertain labels. Recently, many approaches have been proposed for incomplete data, but few of them can handle the case of both missing views and labels. Moreover, these few existing works commonly ignore potentially valuable information about unknown labels or do not sufficiently explore latent label information. Therefore, in this paper, we propose a label semantic-guided contrastive learning method named LSGC for the dual incomplete multi-view multi-label classification problem. Concretely, LSGC employs deep neural networks to extract high-level features of samples. Inspired by the observation of exploiting label correlations to improve the feature discriminability, we introduce a graph convolutional network to effectively capture label semantics. Furthermore, we introduce a new sample-label contrastive loss to explore the label semantic information and enhance the feature representation learning. For missing labels, we adopt a pseudo-label filling strategy and develop a weighting mechanism to explore the confidently recovered label information. We validate the framework on five standard datasets and the experimental results show that our method achieves superior performance in comparison with the state-of-the-art methods.

Spatial Continuity and Nonequal Importance in Salient Object Detection With Image-Category Supervision.

Due to the inefficiency of pixel-level annotations, weakly supervised salient object detection with image-category labels (WSSOD) has been receiving increasing attention. Previous works usually endeavor to generate high-quality pseudolabels to train the detectors in a fully supervised manner. However, we find that the detection performance is often limited by two types of noise contained in pseudolabels: 1) holes inside the object or at the edge and outliers in the background and 2) missing object portions and redundant surrounding regions. To mitigate the adverse effects caused by them, we propose local pixel correction (LPC) and key pixel attention (KPA), respectively, based on two key properties of desirable pseudolabels: 1) spatial continuity, meaning an object region consists of a cluster of adjacent points; and 2) nonequal importance, meaning pixels have different importance for training. Specifically, LPC fills holes and filters out outliers based on summary statistics of the neighborhood as well as its size. KPA directs the focus of training toward ambiguous pixels in multiple pseudolabels to discover more accurate saliency cues. To evaluate the effectiveness of our method, we design a simple yet strong baseline we call weakly supervised saliency detector with Transformer (WSSDT) and unify the proposed modules into WSSDT. Extensive experiments on five datasets demonstrate that our method significantly improves the baseline and outperforms all existing congeneric methods. Moreover, we establish the first benchmark to evaluate WSSOD robustness. The results show that our method can improve detection robustness as well. The code and robustness benchmark are available at https://github.com/Horatio9702/SCNI.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

Functionalized Thienopyrazines on NiOx Film as Self-Assembled Monolayer for Efficient Tin-Perovskite Solar Cells Using a Two-Step Method.

Three functionalized thienopyrazines (TPs), TP-MN (1), TP-CA (2), and TPT-MN (3) were designed and synthesized as self-assembled monolayers (SAMs) deposited on the NiOx film for tin-perovskite solar cells (TPSCs). Thermal, optical, electrochemical, morphological, crystallinity, hole mobility, and charge recombination properties, as well as DFT-derived energy levels with electrostatic surface potential mapping of these SAMs, have been thoroughly investigated and discussed. The structure of the TP-MN (1) single crystal was successfully grown and analyzed to support the uniform SAM produced on the ITO/NiOx substrate. When we used NiOx as HTM in TPSC, the device showed poor performance. To improve the efficiency of TPSC, we utilized a combination of new organic SAMs with NiOx as HTM, the TPSC device exhibited the highest PCE of 7.7 % for TP-MN (1). Hence, the designed NiOx/TP-MN (1) acts as a new model system for the development of efficient SAM-based TPSC. To the best of our knowledge, the combination of organic SAMs with anchoring CN/CN or CN/COOH groups and NiOx as HTM for TPSC has never been reported elsewhere. The TPSC device based on the NiOx/TP-MN bilayer exhibits great enduring stability for performance, retaining ~80 % of its original value for shelf storage over 4000 h.

G272V and P301L Mutations Induce Isoform Specific Tau Mislocalization to Dendritic Spines and Synaptic Dysfunctions in Cellular Models of 3R and 4R Tau Frontotemporal Dementia.

Tau pathologies are detected in the brains of some of the most common neurodegenerative diseases including Alzheimer's disease (AD), Lewy body dementia (LBD), chronic traumatic encephalopathy (CTE), and frontotemporal dementia (FTD). Tau proteins are expressed in six isoforms with either three or four microtubule-binding repeats (3R tau or 4R tau) due to alternative RNA splicing. AD, LBD, and CTE brains contain pathological deposits of both 3R and 4R tau. FTD patients can exhibit either 4R tau pathologies in most cases or 3R tau pathologies less commonly in Pick's disease, which is a subfamily of FTD. Here, we report the isoform-specific roles of tau in FTD. The P301L mutation, linked to familial 4R tau FTD, induces mislocalization of 4R tau to dendritic spines in primary hippocampal cultures that were prepared from neonatal rat pups of both sexes. Contrastingly, the G272V mutation, linked to familial Pick's disease, induces phosphorylation-dependent mislocalization of 3R tau but not 4R tau proteins to dendritic spines. The overexpression of G272V 3R tau but not 4R tau proteins leads to the reduction of dendritic spine density and suppression of mEPSCs in 5-week-old primary rat hippocampal cultures. The decrease in mEPSC amplitude caused by G272V 3R tau is dynamin-dependent whereas that caused by P301L 4R tau is dynamin-independent, indicating that the two tau isoforms activate different signaling pathways responsible for excitatory synaptic dysfunction. Our 3R and 4R tau studies here will shed new light on diverse mechanisms underlying FTD, AD, LBD, and CTE.

Intrinsically Stretchable Organic Thermoelectric Polymers Enabled by Incorporating Fused-Ring Conjugated Breakers.

While research on organic thermoelectric polymers is making significant progress in recent years, realization of a single polymer material possessing both thermoelectric properties and stretchability for the next generation of self-powered wearable electronics is a challenging task and remains an area yet to be explored. A new molecular engineering concept of "conjugated breaker" is employed to impart stretchability to a highly crystalline diketopyrrolepyrrole (DPP)-based polymer. A hexacyclic diindenothieno[2,3-b]thiophene (DITT) unit, with two 4-octyloxyphenyl groups substituted at the tetrahedral sp3-carbon bridges, is selected to function as the conjugated breaker that can sterically hinder intermolecular packing to reduce polymers' crystallinity. A series of donor-acceptor random copolymers is thus developed via polymerizing the crystalline DPP units with the DITT conjugated breakers. By controlling the monomeric DPP/DITT ratios, DITT30 reaches the optimal balance of crystalline/amorphous regions, exhibiting an exceptional power factor (PF) value up to 12.5 µW m-1 K-2 after FeCl3-doping; while, simultaneously displaying the capability to withstand strains exceeding 100%. More significantly, the doped DITT30 film possesses excellent mechanical endurance, retaining 80% of its initial PF value after 200 cycles of stretching/releasing at a strain of 50%. This research marks a pioneering achievement in creating intrinsically stretchable polymers with exceptional thermoelectric properties.

Visible-Light-Promoted Radical Cascade Cyclization of 2-Vinyl Benzimidazoles: Access to Benzo[4,5]imidazo[1,2-b]isoquinolin- 11(6H)-ones.

A visible-light-enabled photoredox radical cascade cyclization of 2-vinyl benzimidazole derivatives is developed. This chemistry is applicable to a wide range of N-aroyl 2-vinyl benzimidazoles as acceptors, and halo compounds, including alkyl halides, acyl chlorides and sulfonyl chlorides, as radical precursors. The Langlois reagent also serves as an effective partner in this photocatalytic oxidative cascade process. This protocol provides a robust alternative for rendering highly functionalized benzo[4,5]imidazo[1,2-b]isoquinolin-11(6H)-ones.

Electrical Double-Layer Transistors Comprising Block Copolymer Electrolytes for Low-Power-Consumption Photodetectors.

Electrical double-layer transistors (EDLTs) have received extensive research attention owing to their exciting advantages of low working voltage, high biocompatibility, and sensitive interfacial properties in ultrasensitive portable sensing applications. Therefore, it is of great interest to reduce photodetectors' operating voltage and power consumption by utilizing photo-EDLT. In this study, a series of block copolymers (BCPs) of poly(4-vinylpyridine)-block-poly(ethylene oxide) (P4VP-b-PEO) with different compositions were applied to formulate polyelectrolyte with indigo carmine salt in EDLT. Accordingly, PEO conduces ion conduction in the BCP electrolyte and enhances the carrier transport capability in the semiconducting channel; P4VP boosts the photocurrent by providing charge-trapping sites during light illumination. In addition, the severe aggregation of PEO is mitigated by forming a BCP structure with P4VP, enhancing the stability and photoresponse of the photo-EDLT. By optimizing the BCP composition, EDLT comprising P4VP16k-b-PEO5k and indigo carmine provides the highest specific detectivity of 2.1 × 107 Jones, along with ultralow power consumptions of 0.59 nW under 450 nm light illumination and 0.32 pW under dark state. The results indicate that photo-EDLT comprising the BCP electrolyte is a practical approach to reducing phototransistors' operating voltage and power consumption.

Engineered coiled-coil HIF1α protein domain mimic.

The development of targeted anti-cancer therapeutics offers the potential for increased efficacy of drugs and diagnostics. Utilizing modalities agnostic to tumor type, such as the hypoxic tumor microenvironment (TME), may assist in the development of universal tumor targeting agents. The hypoxia-inducible factor (HIF), in particular HIF1, plays a key role in tumor adaptation to hypoxia, and inhibiting its interaction with p300 has been shown to provide therapeutic potential. Using a multivalent assembled protein (MAP) approach based on the self-assembly of the cartilage oligomeric matrix protein coiled-coil (COMPcc) domain fused to the critical residues of the C-terminal transactivation domain (C-TAD) of the α subunit of HIF1 (HIF1α), we generate HIF1α-MAP (H-MAP). The resulting H-MAP demonstrates picomolar binding affinity to p300, the ability to downregulate hypoxia-inducible genes, and in vivo tumor targeting capability.

A Lesion-Fusion Neural Network for Multi-View Diabetic Retinopathy Grading.

As the most common complication of diabetes, diabetic retinopathy (DR) is one of the main causes of irreversible blindness. Automatic DR grading plays a crucial role in early diagnosis and intervention, reducing the risk of vision loss in people with diabetes. In these years, various deep-learning approaches for DR grading have been proposed. Most previous DR grading models are trained using the dataset of single-field fundus images, but the entire retina cannot be fully visualized in a single field of view. There are also problems of scattered location and great differences in the appearance of lesions in fundus images. To address the limitations caused by incomplete fundus features, and the difficulty in obtaining lesion information. This work introduces a novel multi-view DR grading framework, which solves the problem of incomplete fundus features by jointly learning fundus images from multiple fields of view. Furthermore, the proposed model combines multi-view inputs such as fundus images and lesion snapshots. It utilizes heterogeneous convolution blocks (HCB) and scalable self-attention classes (SSAC), which enhance the ability of the model to obtain lesion information. The experimental results show that our proposed method performs better than the benchmark methods on the large-scale dataset.

A pH-Sensitive Stretchable Zwitterionic Hydrogel with Bipolar Thermoelectricity.

Amid growing interest in using body heat for electricity in wearables, creating stretchable devices poses a major challenge. Herein, a hydrogel composed of two core constituents, namely the negatively-charged 2-acrylamido-2-methylpropanesulfonic acid and the zwitterionic (ZI) sulfobetaine acrylamide, is engineered into a double-network hydrogel. This results in a significant enhancement in mechanical properties, with tensile stress and strain of up to 470.3 kPa and 106.6%, respectively. Moreover, the ZI nature of the polymer enables the fabrication of a device with polar thermoelectric properties by modulating the pH. Thus, the ionic Seebeck coefficient (Si) of the ZI hydrogel ranges from -32.6 to 31.7 mV K-1 as the pH is varied from 1 to 14, giving substantial figure of merit (ZTi) values of 3.8 and 3.6, respectively. Moreover, a prototype stretchable ionic thermoelectric supercapacitor incorporating the ZI hydrogel exhibits notable power densities of 1.8 and 0.9 mW m-2 at pH 1 and 14, respectively. Thus, the present work paves the way for the utilization of pH-sensitive, stretchable ZI hydrogels for thermoelectric applications, with a specific focus on harvesting low-grade waste heat within the temperature range of 25-40 °C.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

BACKGROUND: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS: We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS: The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS: Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Arrhythmia, also known as an irregular heartbeat, is a common cardiovascular disease. Sometimes the presence of an arrhythmia can increase the risk of more serious heart conditions. Long-term monitoring of the heartbeat enables arrhythmia to be more easily diagnosed. To accurately detect arrhythmia, we developed a computational model that was able to detect six common types of arrhythmias from readings of the heart rate obtained using a device connected to a mobile phone. We showed that our model could diagnose these arrhythmias in over 270,000 people living in China. Our diagnostic system could enable arrhythmias to be diagnosed more easily outside of hospitals and therefore improve access to healthcare, particularly for those in remote settings.

Reversible Molecular Conformation Transitions of Smectic Liquid Crystals for Light/Bias-Gated Transistor Memory.

In recent years, organic photonic field-effect transistors have made remarkable progress with the rapid development of conjugated polycrystalline materials. Liquid crystals, with their smooth surface, defined layer thickness, and crystalline structures, are commonly used for these advantages. In this work, a series of smectic liquid crystalline molecules, 2,9-didecyl-dinaphtho-thienothiophene (C10-DNTT), 2,7-didecyl-benzothieno-benzothiopene (C10-BTBT), 3,9-didecyl-dinaphtho-thiophene (C10-DNT), and didecyl-sexithiophene (C10-6T), have been used in photonic transistor memory, functioning as both hole-transport channels and electron traps to investigate systematically the reasons and mechanisms behind the memory behavior of smectic liquid crystals. After thermal annealing, C10-BTBT and C10-6T/C10-DNTT are homeotropically aligned from the smectic A and smectic X phases, respectively. The 3D-ordered structure of these smectic-aligned crystals contributed to efficient photowriting and electrical erasing processes. Among them, the device performance of C10-BTBT was particularly significant, with a memory window of 21 V. The memory ratio could reach 1.5 × 106 and maintain a memory ratio of over 3 orders after 10,000 s, contributing to its smectic A structure. Through the research, we confirmed the memory and light/bias-gated behaviors of these smectic liquid crystalline molecules, attributing them to reversible molecular conformation transitions and the inherent structural inhomogeneity inside the polycrystalline channel layer.

Bicyclopentadithiophene-Based Organic Semiconductor for Stable and High-Performance Perovskite Solar Cells Exceeding 22.

Well-performing organic-inorganic halide perovskites are susceptible to poor efficiency and instability due to their various defects at the interphases, grain boundaries (GBs), and surfaces. In this study, an in situ method is utilized for effectively passivating the under-coordinated Pb2+ defects of perovskite with new non-fullerene acceptors (NFAs) (INXBCDT; X = H, Cl, and Br) through their carbonyl and cyano functional groups during the antisolvent dripping process. It reveals that the bicyclopentadithiophene (BCDT) core with highly electron-withdrawing end-capping groups passivates GBs and boosts perovskite grain growth. This effective defect passivation decreases the trap density to increase the carrier recombination lifetime of the perovskite film. As a result, bromo-substituted dicyanomethylene indanone (INBr)-end-capped BCDT (INBrBCDT-b8; 3a)-passivated devices exhibit the highest power conversion efficiency (PCE) of 22.20% (vs those of 18.09% obtained for perovskite films without passivation) upon an optimized film preparation process. Note that devices treated with more soluble 2-ethylhexyl-substituted compounds (1a, 2a, and 3a) exhibit higher PCE than those treated with less soluble octyl-substituted compounds (1b, 2b, and 3b). It is also worth noting that BCDT is a cost-effective six-ring core that is easier to synthesize with a higher yield and therefore much cheaper than those with highly fused-ring cores. In addition, a long-term stability test in a glovebox for 1500 h reveals that the perovskite solar cells (PSCs) based on a perovskite absorber treated with compound 3a maintain ∼90% of their initial PCE. This is the first example of the simplest high-conjugation additive for perovskite film to achieve a PCE greater than 22% of the corresponding lead-based PSCs.

Enhanced Electrical Conductivity and Mechanical Properties of Stretchable Thermoelectric Generators Formed by Doped Semiconducting Polymer/Elastomer Blends.

Recent research efforts have concentrated on the development of flexible and stretchable thermoelectric (TE) materials. However, significant challenges have emerged, including increased resistance and reduced electrical conductivity when subjected to strain. To address these issues, rigid semiconducting polymers and elastic insulating polymers have been incorporated and nanoconfinement effects have been exploited to enhance the charge mobility. Herein, a feasible approach is presented for fabricating stretchable TE materials by using a doped semiconducting polymer blend consisting of either poly(3-hexylthiophene) (P3HT) or poly(3,6-dithiophen-2-yl-2,5-di(2-decyltetradecyl)-pyrrolo[3,4-c]pyrrole-1,4-dione-alt-thienylenevinylene-2,5-yl) (PDVT-10) as the rigid polymer with styrene-ethylene-butylene-styrene (SEBS) as the elastic polymer. In particular, the blend composition is optimized to achieve a continuous network structure with SEBS, thereby improving the stretchability. The optimized polymer films exhibit well-ordered microstructural aggregates, indicative of good miscibility with FeCl3 and enhanced doping efficiency. Notably, a lower activation energy and higher charge-carrier concentration contribute to an improved electrical conductivity under high tensile strain, with a maximum output power of 1.39 nW at a ΔT of 22.4 K. These findings offer valuable insights and serve as guidelines for the development of stretchable p-n junction thermoelectric generators based on doped semiconducting polymer blends with potential applications in wearable electronics and energy harvesting.

Synergistic Interactions in Sequential Process Doping of Polymer/Single-Walled Carbon Nanotube Nanocomposites for Enhanced n-Type Thermoelectric Performance.

This study focuses on the fabrication of nanocomposite thermoelectric devices by blending either a naphthalene-diimide (NDI)-based conjugated polymer (NDI-T1 or NDI-T2), or an isoindigo (IID)-based conjugated polymer (IID-T2), with single-walled carbon nanotubes (SWCNTs). This is followed by sequential process doping method with the small molecule 4-(2,3-dihydro-1,3-dimethyl-1H-benzimidazol-2-yl)-N,N-dimethylbenzenamine (N-DMBI) to provide the nanocomposite with n-type thermoelectric properties. Experiments in which the concentrations of the N-DMBI dopant are varied demonstrate the successful conversion of all three polymer/SWCNT nanocomposites from p-type to n-type behavior. Comprehensive spectroscopic, microstructural, and morphological analyses of the pristine polymers and the various N-DMBI-doped polymer/SWCNT nanocomposites are performed in order to gain insights into the effects of various interactions between the polymers and SWCNTs on the doping outcomes. Among the obtained nanocomposites, the NDI-T1/SWCNT exhibits the highest n-type Seebeck coefficient and power factor of -57.7 µV K-1 and 240.6 µW m-1 K-2 , respectively. However, because the undoped NDI-T2/SWCNT exhibits a slightly higher p-type performance, an integral p-n thermoelectric generator is fabricated using the doped and undoped NDI-T2/SWCNT nanocomposite. This device is shown to provide an output power of 27.2 nW at a temperature difference of 20 K.

Diselenophene-Dithioalkylthiophene Based Quinoidal Small Molecules for Ambipolar Organic Field Effect Transistors.

This work presents a series of novel quinoidal organic semiconductors based on diselenophene-dithioalkylthiophene (DSpDST) conjugated cores with various side-chain lengths (-thiohexyl, -thiodecyl, and -thiotetradecyl, designated DSpDSTQ-6, DSpDSTQ-10, and DSpDSTQ-14, respectively). The purpose of this research is to develop solution-processable organic semiconductors using dicyanomethylene end-capped organic small molecules for organic field effect transistors (OFETs) application. The physical, electrochemical, and electrical properties of these new DSpDSTQs are systematically studied, along with their performance in OFETs and thin film morphologies. Additionally, the molecular structures of DSpDSTQ are determined through density functional theory (DFT) calculations and single-crystal X-ray diffraction analysis. The results reveal the presence of intramolecular S (alkyl)···Se (selenophene) interactions, which result in a planar SR-containing DSpDSTQ core, thereby promoting extended π-orbital interactions and efficient charge transport in the OFETs. Moreover, the influence of thioalkyl side chain length on surface morphologies and microstructures is investigated. Remarkably, the compound with the shortest thioalkyl chain, DSpDSTQ-6, demonstrates ambipolar carrier transport with the highest electron and hole mobilities of 0.334 and 0.463 cm2 V-1 s-1 , respectively. These findings highlight the excellence of ambipolar characteristics of solution-processable OFETs based on DSpDSTQs even under ambient conditions.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure.

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.