A stretchable, flexible, and bendable random laser system capable of lasing in a wide range of spectrum will have many potential applications in next- generation technologies, such as visible-spectrum communication, superbright solid-state lighting, biomedical studies, fluorescence, etc. However, producing an appropriate cavity for such a wide spectral range remains a challenge owing to the rigidity of the resonator for the generation of coherent loops. 2D materials with wrinkled structures exhibit superior advantages of high stretchability and a suitable matrix for photon trapping in between the hill and valley geometries compared to their flat counterparts. Here, the intriguing functionalities of wrinkled reduced graphene oxide, single-layer graphene, and few-layer hexagonal boron nitride, respectively, are utilized to design highly stretchable and wearable random laser devices with ultralow threshold. Using methyl-ammonium lead bromide perovskite nanocrystals (PNC) to illustrate the working principle, the lasing threshold is found to be ≈10 µJ cm , about two times less than the lowest value ever reported. In addition to PNC, it is demonstrated that the output lasing wavelength can be tuned using different active materials such as semiconductor quantum dots. Thus, this study is very useful for the future development of high-performance wearable optoelectronic devices.

The many distinct advantages of random lasers focused efforts on developing a breakthrough from optical pumping to electrical pumping. However, progress in these is limited due to high optical loss and low gain. In this work, we demonstrate an electrically pumped quantum dot (QD) random laser with visible emission based on a previously unexplored paradigm named coherent Förster resonance energy transfer (CFRET). In the CFRET process, when a coherent photonic mode is formed because of multiple scattering of the emitted light traveling in mixed donor and acceptor QDs, the donor QDs not only serve as scattering centers but are also enable coherent energy transfer to acceptor QDs. Therefore, the laser action can be easily achieved, and the lasing threshold is greatly reduced. Our approach of electrically pumped QD-based random lasers represents a substantial step toward a full-spectrum random laser for practical applications.

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Multifunctional lanthanide-doped upconversion nanoparticles (UCNPs) have spread their wings in the fields of flexible optoelectronics and biomedical applications. One of the ongoing challenges lies in achieving UCNP-based nanocomposites, which enable a continuous-wave (CW) laser action at ultralow thresholds. Here, gold sandwich UCNP nanocomposites [gold (Au1)–UCNP–gold (Au2)] capable of exhibiting lasing at ultralow thresholds under CW excitation are demonstrated. The metastable energy-level characteristics of lanthanides are advantageous for creating population inversion. In particular, localized surface plasmon resonance-based electromagnetic hotspots in the nanocomposites and the huge enhancement of scattering coefficient for the formation of coherent closed loops due to multiple scattering facilitate the process of stimulated emissions as confirmed by theoretical simulations. The nanocomposites are subjected to stretchable systems for enhancing the lasing action (threshold ∼ 0.06 kW cm–2) via a light-trapping effect. The applications in bioimaging of HeLa cells and antibacterial activity (photothermal therapy) are demonstrated using the newly designed Au1–UCNP–Au2 nanocomposites.

Objective This study aimed to establish a deep learning method based on convolutional networks for the preliminary study of the pathological diagnosis of prosthetic joint infections (PJI). Methods We enrolled 20 revision patients after joint replacement from the Department of Orthopedics, the First Medical Center, General Hospital of the People's Liberation Army, from January 2021 to January 2022 (10 of whom were confirmed to be infected against 2018 ICM criteria, and the remaining 10 were verified to be non-infected), and classified high-power field images according to 2018 ICM criteria. Then, we inputted 576 positive images and 576 negative images into a neural network by employing a resNET model, used to select 461 positive images and 461 negative images as training sets, 57 positive images and 31 negative images as internal verification sets, 115 positive images and 115 negative images as external test sets. Results The resNET model classification was used to analyze the pathological sections of PJI patients under high magnification fields. The results of internal validation set showed a positive accuracy of 96.49%, a negative accuracy of 87.09%, an average accuracy of 93.22%, an average recall rate 96.49%, and an F1 of 0.9482. The accuracy of external test results was 97.39% positive, 93.04% negative, the average accuracy of external test set was 93.33%, the average recall rate was 97.39%, with an F1 of 0.9482. The AUC area of the intelligent image-reading diagnosis system was 0.8136. Conclusions This study used the convolutional neural network deep learning to identify high-magnification images from pathological sections of soft tissues around joints, against the diagnostic criteria for acute infection, and a high precision and a high recall rate were accomplished. The results of this technique confirmed that better results could be achieved by comparing the new method with the standard strategies in terms of diagnostic accuracy. Continuous upgrading of extended training sets is needed to improve the diagnostic accuracy of the convolutional network deep learning before it is applied to clinical practice.