Managing excess lead iodideIn hybrid perovskite solar cells, the formation of lead iodide (PbI2) can provide some passivation effects but can lead to device instability and hysteresis in ...current–density changes with voltage. Zhao et al. show that doping with rubidium chloride (RbCl) can create a passive inactive (PbI2)2RbCl phase that stabilizes the perovskite phase and lowers its bandgap. Devices exhibited 25.6% certified power efficiency and maintained 80% of that efficiency after 500 hours of operation at 85°C. —PDS
The explosive success of graphene opens a new era of ultrathin 2D materials. It has been realized that the van der Waals layered materials with atomic and less atomic thickness can not only exist ...stably, but also exhibit unique and technically useful properties including small size effect, surface effect, macro quantum tunnel effect, and quantum effect. With the extensive research and revealing of the basic optical properties and new photophysical properties of 2D materials, a series of potential applications in optical devices have been continuously demonstrated and realized, which immediately roused an upsurge of study in the academic circle. Therefore, the application of 2D materials as broadband, efficient, convenient, and versatile saturable absorbers in ultrafast lasers is a potential and promising field. Herein, the main preparation methods of 2D materials are reviewed and technical guidelines for identifying and characterizing layered 2D materials are provided. After investigating the characteristics of 2D materials thoroughly in nonlinear optics, their performances in fiber lasers are comprehensively summarized according to the types of materials. Finally, some developmental challenges, potential prospects, and future research directions are summarized and presented for such promising materials.
The effects of material thickness on optical nonlinearity are studied as an important subject recently. Here, thickness dependent nonlinear absorption properties of 2D materials in fiber lasers are presented. Those thickness‐dependent photonic devices are successfully applied in fiber lasers to achieve Q‐switched and mode‐locked operation. Experiments prove that fiber lasers based on those devices have excellent performance in ultrafast optics.
2D elemental layered crystals, such as graphene and black phosphorus (B‐P), have received tremendous attentions due to their rich physical and chemical properties. In the applications of ...nanoelectronic devices, graphene shows super high electronic mobility, but it lacks bandgap which impedes development in logical devices. As an alternative, B‐P shows high mobility of up to about 1000 cm2 V−1 s−1. However, B‐P is very unstable and degrades rapidly in ambient conditions. Orthorhombic arsenic (black arsenic; b‐As) is the “cousin” of B‐P; theoretical prediction shows that b‐As also has excellent physical and chemical properties, but there is almost no experimental report on b‐As. Herein, it is reported on the unique transport characteristics and stability of monolayer and few‐layer b‐As crystals which are exfoliated from the natural mineral. The properties of field‐effect transistors (FETs) strongly depend on the thickness of crystals. In the monolayer limit, the performance shows relatively high carrier mobilities and large on/off ratios. Moreover, the b‐As crystals exhibit a relatively good ambient stability. The few‐layer arsenic based FET still function after exposure to air for about one month. Therefore, b‐As is expected to be a promising 2D material candidate in nanoelectronic devices.
A black arsenic monolayer is synthesized from the natural mineral of orthorhombic arsenic by mechanical exfoliation for the first time. Monolayer black arsenic (b‐As) based field‐effect transistors exhibit good carrier transport properties. Meanwhile, b‐As exhibits a better ambient stability than black phosphorus.
Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of ...materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application.
Machine learning has been widely used in various fields of materials science. This review focused on the basic operational procedures of machine learning in analyzing the properties of materials; it summarized the applications of machine learning algorithms in materials science in recent years, which include material property analysis, materials design, and quantum chemistry; and it discussed problems and possible new directions in the development of machine learning.
The identification of prognostic genes that can distinguish the prognostic risks of cancer patients remains a significant challenge. Previous works have proven that functional gene sets were more ...reliable for this task than the gene signature. However, few works have considered the cross-talk among functional gene sets, which may result in neglecting important prognostic gene sets for cancer.
Here, we proposed a new method that considers both the interactions among modules and the prognostic correlation of the modules to identify prognostic modules in cancers. First, dense sub-networks in the gene co-expression network of cancer patients were detected. Second, cross-talk between every two modules was identified by a permutation test, thus generating the module network. Third, the prognostic correlation of each module was evaluated by the resampling method. Then, the GeneRank algorithm, which takes the module network and the prognostic correlations of all the modules as input, was applied to prioritize the prognostic modules. Finally, the selected modules were validated by survival analysis in various data sets. Our method was applied in three kinds of cancers, and the results show that our method succeeded in identifying prognostic modules in all the three cancers. In addition, our method outperformed state-of-the-art methods. Furthermore, the selected modules were significantly enriched with known cancer-related genes and drug targets of cancer, which may indicate that the genes involved in the modules may be drug targets for therapy.
We proposed a useful method to identify key modules in cancer prognosis and our prognostic genes may be good candidates for drug targets.
Reactive oxygen species (ROS) depletion and low ROS production that result from the intratumoral redox metabolism equilibrium and low energy conversion efficiency from ultrasound mechanical energy to ...ROS‐represented chemical energy, respectively, are two vital inhibitory factors of sonodynamic therapy (SDT). To address the two concerns, a tumor metabolism‐engineered composite nanoplatform capable of intervening intratumoral ROS metabolism, breaking the redox equilibrium, and reshaping the tumor microenvironment is constructed to reinforce SDT against tumors. In this metabolism‐engineered nanoplatform, Nb2C nanosheets serve as the scaffold to accommodate TiO2 sonosensitizers and l‐buthionine‐sulfoximine. Systematic experiments show that such nanoplatforms can reduce ROS depletion via suppressing glutathione synthesis and simultaneously improving ROS production via the Nb2C‐enhanced production and separation of electron–hole pairs. Contributed by the combined effect, net ROS content can be significantly elevated, which results in the highly efficient anti‐tumor outcomes in vivo and in vitro. Moreover, the combined design principles, that is, tumor metabolism modulation for reducing ROS depletion and electron–hole pair separation for facilitating ROS production, can be extended to other ROS‐dependent therapeutic systems.
An intratumoral metabolism modulation‐engineered sonodynamic therapy (SDT)‐based nanoplatform has been constructed to break the reactive oxygen species (ROS)‐involved redox metabolism equilibrium and reshape the tumor microenvironment for reducing ROS depletion, and simultaneously facilitate ROS production via enhancing the production and separation of electron–hole pairs, which enables the significantly improved net content of ROS for highly‐efficient SDT against tumors.
2D materials have been attracting high interest in recent years due to their low structural symmetry, excellent photoresponse, and high air stability. However, most 2D materials can only respond to ...specific light, which limits the development of wide‐spectrum photodetectors. Proper bandgap and the regulation of Fermi level are the foundations for realizing electronic multichannel transition, which is an effective method to achieve a wide spectral response. Herein, a noble 2D material, palladium phosphide sulfide (PdPS), is designed and synthesized. The bandgap of PdPS is around 2.1 eV and the formation of S vacancies, interstitial Pd and P atoms promote the Fermi level very close to the conduction band. Therefore, the PdPS‐based photodetector shows impressive wide spectral response from solar‐blind ultraviolet to near‐infrared based on the multichannel transition. It also exhibits superior optoelectrical properties with photoresponsivity (R) of 1 × 103 A W−1 and detectivity (D*) of 4 × 1011 Jones at 532 nm. Moreover, PdPS exhibits good performance of polarization detection with dichroic ratio of ≈3.7 at 808 nm. Significantly, it achieves polarimetric imaging and hidden‐target detection in complex environments through active detection.
A wide spectral response photodetector and polarimetric image sensor based on 2D palladium phosphide sulfide (PdPS) are designed. The photoresponse range of PdPS is from solar‐blind ultraviolet to the near‐infrared. The PdPS‐based polarimetric imaging sensor achieves object imaging and hidden‐target detection in complex environments.
A rhodium(III)‐catalyzed domino annulation of simple olefins with diazo oxindoles to give spirooxindole pyrrolone products is described. This reaction can be formally viewed as the result of an ...anomalous tandem C−H activation, carbene insertion, Lossen rearrangement, and a nucleophilic addition process. The potential utility of this reaction was further demonstrated by the late‐stage diversification of drug molecules.
Domino process: A RhIII‐catalyzed domino annulation of simple olefins with diazo oxindoles generates spirooxindole dihydropyrrole products. This reaction can be formally viewed as the result of a C−H activation, carbene insertion, and Lossen rearrangement, and was applied to the late‐stage diversification of drug molecules.
The intentionally designed band alignment of heterostructures and doping engineering are keys to implement device structure design and device performance optimization. According to the theoretical ...prediction of several typical materials among the transition metal dichalcogenides (TMDs) and group‐IV metal chalcogenides, MoS2 and SnSe2 present the largest staggered band offset. The large band offset is conducive to the separation of photogenerated carriers, thus MoS2/SnSe2 is a theoretically ideal candidate for fabricating photodetector, which is also verified in the experiment. Furthermore, in order to extend the photoresponse spectrum to solar‐blind ultraviolet (SBUV), doping engineering is adopted to form an additional electron state, which provides an extra carrier transition channel. In this work, pure MoS2/SnSe2 and doped MoS2/SnSe2 heterostructures are both fabricated. In terms of the photoelectric performance evaluation, the rejection ratio R254/R532 of the photodetector based on doped MoS2/SnSe2 is five orders of magnitude higher than that of pure MoS2/SnSe2, while the response time is obviously optimized by 3 orders. The results demonstrate that the combination of band alignment and doping engineering provides a new pathway for constructing SBUV photodetectors.
The staggered large band offset heterojunction based on MoS2 and SnSe2 is an ideal structure for the separation of photogenerated carriers. Combined with doping engineering, a photodetector based on the doped MoS2/SnSe2 heterostructure exhibits excellent solar‐blind UV photoresponse ability compared to the pure MoS2/SnSe2 heterostructure. The rejection ratio is significantly improved by about five orders of magnitude.
For a target task where the labeled data are unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a global domain ...shift, i.e., align the global source and target distributions without considering the relationships between two subdomains within the same category of different domains, leading to unsatisfying transfer learning performance without capturing the fine-grained information. Recently, more and more researchers pay attention to subdomain adaptation that focuses on accurately aligning the distributions of the relevant subdomains. However, most of them are adversarial methods that contain several loss functions and converge slowly. Based on this, we present a deep subdomain adaptation network (DSAN) that learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD). Our DSAN is very simple but effective, which does not need adversarial training and converges fast. The adaptation can be achieved easily with most feedforward network models by extending them with LMMD loss, which can be trained efficiently via backpropagation. Experiments demonstrate that DSAN can achieve remarkable results on both object recognition tasks and digit classification tasks. Our code will be available at https://github.com/easezyc/deep-transfer-learning .
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