Semiconducting polymers (SPs) have shown great feasibility as candidates for near-infrared-II (NIR-II) fluorescence imaging-navigated photothermal therapy due to their strong light-harvesting ability and flexible tunability. However, the fluorescence signal of traditional SPs tends to quench in their aggregate states owing to the strong π–π stacking, which can lead to the radiative decay pathway shutting down. To address this issue, aggregation-induced emission effect has been used as a rational tactic to boost the aggregate-state fluorescence of NIR-II emitters. In this contribution, we developed a precise molecular engineering tactic based on the block copolymerizations that integrate planar and twisted segments into one conjugated polymer backbone, providing great flexibility in tuning the photophysical properties and photothermal conversion capacity of SPs. Two monomers featured with twisted and planar architectures, respectively, were tactfully incorporated via a ternary copolymerization approach to produce a series of new SPs. The optimal copolymer (SP2) synchronously shows desirable absorption ability and good NIR-II quantum yield on the premise of maintaining typical aggregation-induced emission characteristics, resulting in balanced NIR-II fluorescence brightness and photothermal property. Water-dispersible nanoparticles fabricated from the optimal SP2 show efficient photothermal therapeutic effects both in vitro and in vivo. The in vivo investigation reveals the distinguished NIR-II fluorescence imaging performance of SP2 nanoparticles and their photothermal ablation toward tumor with prominent tumor accumulation ability and excellent biocompatibility.

Recent advancements in two-dimensional materials have shown huge potential for optoelectronic applications. It is challenging to achieve highly effective and sensitive broadband photodetection based on MoS₂ devices. Defect engineering, such as introducing vacancies, can narrow the bandgap and boost the separation of photogenerated carriers by defect states but leads to a slow response speed. Herein, we propose a nickel nanoparticle-induced gateless photogating effect with a unique energy band structure to enable the application of defect engineering and achieve high optoelectronic performance. The device based on Ni nanoparticle-decorated MoS₂ with S vacancies exhibited high responsivities of 106.21 and 1.38 A W⁻¹ and detectivities of 1.9 × 10¹² and 8.9 × 10⁹ Jones under 532 and 980 nm illumination (visible to near infrared), respectively, with highly accelerated response speed. This strategy provides new insight into optimizing defect engineering to design high-performance optoelectronic devices capable of broadband photodetection.

Wide-bandgap (WBG) perovskite solar cells (PSCs) are essential for highly efficient and stable silicon/perovskite tandem solar cells. In this study, we adopted a synthetic strategy with lead thiocyanate (Pb(SCN)₂) additive and methylammonium chloride (MACl) posttreatment to enhance the crystallinity and improve the interface of WBG perovskite films with a bandgap of 1.68 eV. The excessive PbI₂ was formed at grain boundaries and converted into MAPbI_3−xCl_x perovskites, which are utilized to form the graded heterojunction (GHJ) and compressive strain. This is beneficial for passivating nonradiative recombination defects, suppressing halide phase segregation, and facilitating carrier extraction. Subsequently, the device with GHJ delivered a champion efficiency of 20.30% and superior stability in ambient air and under 85 °C. Finally, we achieved a recorded efficiency of 30.91% for 4-terminal WBG perovskite/TOPCon tandem silicon solar cells. Our findings demonstrate a promising approach for fabricating efficient and stable WBG PSCs through the formation of GHJ.

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

The increasingly aging society led to a rise in the prevalence of chronic wounds (CWs), posing a significant burden to public health on a global scale. One of the key features of CWs is the presence of a maladjusted immune microenvironment characterized by persistent and excessive (hyper)inflammation. A variety of immunomodulatory therapies have been proposed to address this condition. Yet, to date, current delivery systems for immunomodulatory therapy remain inadequate and lack efficiency. This highlights the need for new therapeutic delivery systems, such as nanosystems, to manage the pathological inflammatory imbalance and, ultimately, improve the treatment outcomes of CWs. While a plethora of immunomodulatory nanosystems modifying the immune microenvironment of CWs have shown promising therapeutic effects, the literature on the intersection of immunomodulatory nanosystems and CWs remains relatively scarce. Therefore, this review aims to provide a comprehensive overview of the pathogenesis and characteristics of the immune microenvironment in CWs, discuss important advancements in our understanding of CW healing, and delineate the versatility and applicability of immunomodulatory nanosystems-based therapies in the therapeutic management of CWs. In addition, we herein also shed light on the main challenges and future perspectives in this rapidly evolving research field.

High-performance photodetectors hold promising potential in optical communication and imaging systems. However, conventional counterparts are suffering narrow detection range, high power consumption, and poor polarization sensitivity. Characteristics originating from switchable polarization in ferroelectrics can be used to optimize the photo-to-electric procedure and improve the photodetection performance. In this regard, we constructed a configuration by integrating 2-dimensional molybdenum disulfide (MoS₂) with ferroelectric lithium niobate (LiNbO₃), resulting in the MoS₂/LiNbO₃ heterostructured photodetector. Benefiting from the pyroelectric effect of LiNbO₃, the limitation of bandgap on the detection range can be broken, thus broadening the response band of the detector to 365 to 1,064 nm, as well as enabling the self-powered characteristic. Meanwhile, high carrier mobility and decent light absorbance of MoS₂ introduce robust light-matter interactions with the underlying LiNbO₃, leading to ultrafast rise/fall times of ≈150 μs/250 μs and switching ratios of up to ≈190. Moreover, the highest responsivity, specific detectivity, and external quantum efficiency achieved were 17.3 A·W⁻¹, 4.3 × 10¹¹ Jones, and 4,645.78%, respectively. Furthermore, because of the anisotropy of the spontaneous-polarized LiNbO₃ substrate, the photocurrent of the device achieved a dichroic ratio of 7.42, comparing favorably to most MoS₂-based photodetectors. This work demonstrates the integration potential between ferroelectric LiNbO₃ and 2-dimensional materials for high-performance photodetection.

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

Flowing water can be used as an energy source for generators, providing a major part of the energy for daily life. However, water is rarely used for information or electronic devices. Herein, we present the feasibility of a polarized liquid-triggered photodetector in which polarized water is sandwiched between graphene and a semiconductor. Due to the polarization and depolarization processes of water molecules driven by photogenerated carriers, a photo-sensitive current can be repeatedly produced, resulting in a high-performance photodetector. The response wavelength of the photodetector can be fine-tuned as a result of the free choice of semiconductors as there is no requirement of lattice match between graphene and the semiconductors. Under zero voltage bias, the responsivity and specific detectivity of Gr/NaCl (0.5 M)W/N-GaN reach values of 130.7 mA/W and 2.3 × 10⁹ Jones under 350 nm illumination, respectively. Meanwhile, using a polar liquid photodetector can successfully read the photoplethysmography signals to produce accurate oxygen blood saturation and heart rate. Compared with the commercial pulse oximetry sensor, the average errors of oxygen saturation and heart rate in the designed photoplethysmography sensor are ~1.9% and ~2.1%, respectively. This study reveals that water can be used as a high-performance photodetector in informative industries.

Familial hypercholesterolemia (FH) is a frequently occurring genetic disorder that is linked to early-onset cardiovascular disease. If left untreated, patients with this condition can develop severe cardiovascular complications. Unfortunately, many patients remain undiagnosed, and even when diagnosed, the treatment is often not optimal. Although mutations in the LDLR gene are the primary cause of FH, predicting whether novel variants are pathogenic is not a straightforward task. Understanding the functionality of LDLR variants is crucial in uncovering the genetic basis of FH. Our study utilized CRISPR/Cas9 cytosine base editors in pooled screens to establish a novel approach for functionally assessing tens of thousands of LDLR variants on a large scale. A total of more than 100 single guide RNAs (sgRNAs) targeting LDLR pathogenic mutations were successfully screened with relatively high accuracy. Out of these, 5 sgRNAs were further subjected to functional verification studies, including 1 in the promoter, 1 in the antisense RNA, 1 in the exon, and 2 in the intron. Except for the variant caused by the sgRNA located at intron 16, the functionalities of the other LDLR variants were all downregulated. The high similarity of LDLR intron sequences may lead to some false positives. Overall, these results confirm the reliability of the large-scale screening strategy for functional analysis of LDLR variants, and the screened candidate pathogenic mutations could be used as an auxiliary means of clinical gene detection to prevent FH-induced heart disease.

Infectious diseases severely threaten public health and global biosafety. In addition to transmission through the air, pathogenic microorganisms have also been detected in environmental liquid samples, such as sewage water. Conventional biochemical detection methodologies are time-consuming and cost-ineffective, and their detection limits hinder early diagnosis. In the present study, ultrafine plasmonic fiber probes with a diameter of 125 μm are fabricated for clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas)-12a-mediated sensing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Single-stranded DNA exposed on the fiber surface is trans-cleaved by the Cas12a enzyme to release gold nanoparticles that are immobilized onto the fiber surface, causing a sharp reduction in the surface plasmon resonance (SPR) wavelength. The proposed fiber probe is virus-specific with the limit of detection of ~2,300 copies/ml, and genomic copy numbers can be reflected as shifts in wavelengths. A total of 21 sewage water samples have been examined, and the data obtained are consistent with those of quantitative polymerase chain reaction (qPCR). In addition, the Omicron variant and its mutation sites have been fast detected using S gene-specific Cas12a. This study provides an accurate and convenient approach for the real-time surveillance of microbial contamination in sewage water.