2021 Journal Impact Factor: 11.036

Research ranks #10 among 73 journals in the multidisciplinary category in Web of Science. The journal also has a JCI of 1.58 and a CiteScore of 10.5.

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Journal profile

The open access journal Research, published in association with CAST, publishes innovative, wide-ranging research in life sciences, physical sciences, engineering and applied science.

Editorial board

Research's Editorial Board includes international experts in fields ranging from life sciences to physical sciences. Tianhong Cui of University of Minnesota and Weimin Bao of China Association for Science and Technology serve as the Editors-in-Chief of the journal.

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Research Article

Whether and When Superhydrophobic/Superoleophobic Surfaces Are Fingerprint Repellent

Driven by the ever-increasing demand for fingerprint-resistant techniques in modern society, numerous researches have proposed to develop innovative antifingerprint coatings based on superhydrophobic/superoleophobic surface design. However, whether superhydrophobic/superoleophobic surfaces have favorable repellency to the microscopic fingerprint is in fact an open question. Here, we establish a reliable method that enables evaluating the antifingerprint capability of various surfaces in a quantitative way. We show that superhydrophobicity is irrelevant with fingerprint repellency. Regarding superoleophobic surfaces, two distinct wetting states of microscopic fingerprint residues, i.e., the “repellent” and the “collapsed” states, are revealed. Only in the “repellent” state, in which the fingerprint residues remain atop surface textures upon being pressed, superoleophobic surfaces can bring about favorable antifingerprint repellency, which correlates positively with their receding contact angles. A finger-deformation-dependent intrusion mechanism is proposed to account for the formation of different fingerprint wetting states. Our findings offer important insights into the mechanism of fingerprint repellency and will help the design of high-performance antifingerprint surfaces for diverse applications.


Light-Emitting Artificial Synapses for Neuromorphic Computing

As the key connecting points in the neuromorphic computing systems, synaptic devices have been investigated substantially in recent years. Developing optoelectronic synaptic devices with optical outputs is becoming attractive due to many benefits of optical signals in systems. Colloidal quantum dots (CQDs) are potential luminescent materials for information displays. Light-emitting diodes based on CQDs have become appealing candidates for optoelectronic synaptic devices. Moreover, light-emitting transistors exhibit great application potential in these synaptic devices. From this perspective, light-emitting artificial synapses were discussed on the basis of these structures in the devices. Their mechanisms, performance, and future development were analysed and prospected in detail.

Review Article

Advances in Delivering Oxidative Modulators for Disease Therapy

Oxidation modulators regarding antioxidants and reactive oxygen species (ROS) inducers have been used for the treatment of many diseases. However, a systematic review that refers to delivery system for divergent modulation of oxidative level within the biomedical scope is lacking. To provide a comprehensive summarization and analysis, we review pilot designs for delivering the oxidative modulators and the main applications for inflammatory treatment and tumor therapy. On the one hand, the antioxidants based delivery system can be employed to downregulate ROS levels at inflammatory sites to treat inflammatory diseases (e.g., skin repair, bone-related diseases, organ dysfunction, and neurodegenerative diseases). On the other hand, the ROS inducers based delivery system can be employed to upregulate ROS levels at the tumor site to kill tumor cells (e.g., disrupt the endogenous oxidative balance and induce lethal levels of ROS). Besides the current designs of delivery systems for oxidative modulators and the main application cases, prospects for future research are also provided to identify intelligent strategies and inspire new concepts for delivering oxidative modulators.

Research Article

Tunable Multicolor Fluorescence of Perovskite-Based Composites for Optical Steganography and Light-Emitting Devices

Multicolor fluorescence of mixed halide perovskites enormously enables their applications in photonics and optoelectronics. However, it remains an arduous task to obtain multicolor emissions from perovskites containing single halogen to avoid phase segregation. Herein, a fluorescent composite containing Eu-based metal-organic frameworks (MOFs), 0D Cs4PbBr6, and 3D CsPbBr3 is synthesized. Under excitations at 365 nm and 254 nm, the pristine composite emits blue (B) and red (R) fluorescence, which are ascribed to radiative defects within Cs4PbBr6 and 5D07FJ transitions of Eu3+, respectively. Interestingly, after light soaking in the ambient environment, the blue fluorescence gradually converts into green (G) emission due to the defect repairing and 0D-3D phase conversion. This permanent and unique photochromic effect enables anticounterfeiting and microsteganography with increased security through a micropatterning technique. Moreover, the RGB luminescence is highly stable after encapsulation by a transparent polymer layer. Thus, trichromatic light-emitting modules are fabricated by using the fluorescent composites as color-converting layers, which almost fully cover the standard color gamut. Therefore, this work innovates a strategy for construction of tunable multicolor luminescence by manipulating the radiative defects and structural dimensionality.

Research Article

Shared Core Microbiome and Functionality of Key Taxa Suppressive to Banana Fusarium Wilt

Microbial contributions to natural soil suppressiveness have been reported for a range of plant pathogens and cropping systems. To disentangle the mechanisms underlying suppression of banana Panama disease caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc4), we used amplicon sequencing to analyze the composition of the soil microbiome from six separate locations, each comprised of paired orchards, one potentially suppressive and one conducive to the disease. Functional potentials of the microbiomes from one site were further examined by shotgun metagenomic sequencing after soil suppressiveness was confirmed by greenhouse experiments. Potential key antagonists involved in disease suppression were also isolated, and their activities were validated by a combination of microcosm and pot experiments. We found that potentially suppressive soils shared a common core community with relatively low levels of F. oxysporum and relatively high proportions of Myxococcales, Pseudomonadales, and Xanthomonadales, with five genera, Anaeromyxobacter, Kofleria, Plesiocystis, Pseudomonas, and Rhodanobacter being significantly enriched. Further, Pseudomonas was identified as a potential key taxon linked to pathogen suppression. Metagenomic analysis showed that, compared to the conducive soil, the microbiome in the disease suppressive soil displayed a significantly greater incidence of genes related to quorum sensing, biofilm formation, and synthesis of antimicrobial compounds potentially active against Foc4. We also recovered a higher frequency of antagonistic Pseudomonas isolates from disease suppressive experimental field sites, and their protective effects against banana Fusarium wilt disease were demonstrated under greenhouse conditions. Despite differences in location and soil conditions, separately located suppressive soils shared common characteristics, including enrichment of Myxococcales, Pseudomonadales, and Xanthomonadales, and enrichment of specific Pseudomonas populations with antagonistic activity against the pathogen. Moreover, changes in functional capacity toward an increase in quorum sensing, biofilm formation, and antimicrobial compound synthesizing involve in disease suppression.

Research Article

High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach

A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies. Nevertheless, high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome. To this end, we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures (physical cues comprising geometrical, topographical, and mechanical aspects) and the effects of these complex physical cues on stem cell fate decisions. Furthermore, by applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, which allowed for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces, potentially facilitating precision, and personalized tissue engineering applications.