Hofmeister Effect-Enhanced, Nanoparticle-Shielded, Thermally Stable Hydrogels for Anti-UV, Fast-Response, and All-Day-Modulated Smart Windows.

Thermochromic smart windows offer energy-saving potential through temperature-responsive optical transmittance adjustments, yet face challenges in achieving anti-UV radiation, fast response, and high-temperature stability characteristics for long-term use. Herein, the rational design of Hofmeister effect-enhanced, nanoparticle-shielded composite hydrogels, composed of hydroxypropylmethylcellulose (HPMC), poly(N,N-dimethylacrylamide) (PDMAA), sodium sulfate, and polydopamine nanoparticles, for anti-UV, fast-response, and all-day-modulated smart windows is reported. Specifically, a three-dimensional network of PDMAA is created as the supporting skeleton, markedly enhancing the thermal stability of pristine HPMC hydrogels.

Ionic Liquid-Based Electrolyte with Multiple Hydrogen Bonding Network Enabling High-Voltage Stable Proton Batteries Across Wide Temperature Range.

Proton batteries are strong contender for next-generation energy storage due to their high safety and rapid response. However, the narrow electrochemical window of acidic aqueous electrolytes limits their energy density and stability. Here, an ionic liquid (IL)-based electrolyte (EMImOTf-HPO) containing HPO in polar IL solvent 1-ethyl-3-methylimidazolium trifluoromethanesulfonate (EMImOTf) is developed for stable high-voltage energy storage.

Correction: Affective computing for human-machine interaction a bionic organic memristor exhibiting selective activation.

Correction for 'Affective computing for human-machine interaction a bionic organic memristor exhibiting selective activation' by Bingjie Guo , , 2024, https://doi.org/10.1039/D3MH01950K.

Research on Isomorphic Task Transfer Algorithm Based on Knowledge Distillation in Multi-Agent Collaborative Systems.

In response to the increasing number of agents and changing task scenarios in multi-agent collaborative systems, existing collaborative strategies struggle to effectively adapt to new task scenarios. To address this challenge, this paper proposes a knowledge distillation method combined with a domain separation network (DSN-KD). This method leverages the well-performing policy network from a source task as the teacher model, utilizes a domain-separated neural network structure to correct the teacher model's outputs as supervision, and guides the learning of agents in new tasks.

Affective computing for human-machine interaction a bionic organic memristor exhibiting selective activation.

Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions.

Automatic three-dimensional facial symmetry reference plane construction based on facial planar reflective symmetry net.

Objectives: Three-dimensional (3D) facial symmetry analysis is based on the 3D symmetry reference plane (SRP). Artificial intelligence (AI) is widely used in the dental and oral sciences. This study developed a novel deep learning model called the facial planar reflective symmetry net (FPRS-Net) to automatically construct an SRP and established a method for defining a 3D point-cloud region of interest (ROI) and high-dimensional feature computations suitable for this network model.

Magnetized bentonite modified rice straw biochar: Qualitative and quantitative analysis of Cd(II) adsorption mechanism.

Industrialization has caused a significant global issue with cadmium (Cd) pollution. In this study, Biochar (Bc), generated through initial pyrolysis of rice straw, underwent thorough mixing with magnetized bentonite clay, followed by activation with KOH and subsequent pyrolysis. Consequently, a magnetized bentonite modified rice straw biochar (FeO@B-Bc) was successfully synthesized for effective treatment and remediation of this problem.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception.

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films.

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis.

Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study.

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked.

An ultrasmall organic synapse for neuromorphic computing.

High-performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain-inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high-density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far.

Approaching the Zero-Power Operating Limit in a Self-Coordinated Organic Protonic Synapse.

High-performance artificial synapse with nonvolatile memory and low power consumption is a perfect candidate for brainoid intelligence. Unfortunately, due to the energy barrier paradox between ultra-low power and nonvolatile modulation of device conductances, it is still a challenge at the moment to construct such ideal synapses. Herein, a proton-reservoir type 4,4',4″,4'''-(Porphine-5,10,15,20-tetrayl) tetrakis (benzenesulfonic acid) (TPPS) molecule and fabricated organic protonic memristors with device width of 10 µm to 100 nm is synthesized.

Elucidating the interaction between equisetin and human serum albumin: A comprehensive study using spectroscopy, microcalorimetry and molecular docking approaches.

Equisetin, a bioactive compound of marine origin, offers compelling inhibitory properties against HIV-1 transcriptase. To gain comprehensive insights into the interaction of Equisetin with human serum albumin (HSA), we utilized a multipronged approach involving spectroscopy, isothermal titration calorimetry (ITC) and molecular docking. Our fluorescence analyses confirmed that the interaction between Equisetin and HSA results in a significant quenching of HSA's fluorescence, primarily achieved through a dynamic mechanism aided by hydrogen bonding and van der Waals forces.

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications.

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO)]/triphenylamine-containing polymer (BTPA-F) redox system.

Development of artesunate intelligent prodrug liposomes based on mitochondrial targeting strategy.

Breast cancer is the leading cause of cancer-related deaths in women and remains a formidable therapeutic challenge. Mitochondria participate in a myriad of essential cellular processes, such as metabolism, and are becoming an ideal target for cancer therapy. Artemisinin and its derivatives have demonstrated multiple activities in the context of various cancers.

Establishment and evaluation of glucose-modified nanocomposite liposomes for the treatment of cerebral malaria.

Cerebral malaria (CM) is a life-threatening neurological complication caused by Plasmodium falciparum. About 627,000 patients died of malaria in 2020. Currently, artemisinin and its derivatives are the front-line drugs used for the treatment of cerebral malaria.

Undescribed specialised metabolites from the endophytic fungus Emericella sp. XL029 and their antimicrobial activities.

The fungus Emericella sp. XL029 isolated from leaves of Panax notoginseng was investigated for agents with potential antibacterial and antifungal activities using a one strain-many compounds (OSMAC) strategy. Fifteen compounds, including seven undescribed structures, were obtained from this species.

[Research of preparation quality markers of Yulian Tang with anti-inflammatory activity].

The lipopolysaccharide(LPS)-indused RAW264.7 cells inflammation model was used as a carrier to investigated the effects of the preparation quality markers of Yulian Tang with anti-inflammatory activity in vitro. RAW264.

Learning-Based Intrinsic Reflectional Symmetry Detection.

Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection.

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network.

With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition.

A Prediction Model of Health Development Based on Linear Sequential Extreme Learning Machine Algorithm Matrix.

The rapid development of social economy not only increases people's living pressure but also reduces people's health. Looking for a healthy development prediction model has become a domestic concern. Based on the analysis of the influencing factors of health development, this paper looks for a model to predict the development of public health, so as to improve the accuracy of health development prediction.

A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition.

The interference of noise will cause the degradation of image quality, which can have a negative impact on the subsequent image processing and visual effect. Although the existing image denoising algorithms are relatively perfect, their computational efficiency is restricted by the performance of the computer, and the computational process consumes a lot of energy. In this paper, we propose a method for image denoising and recognition based on multi-conductance states of memristor devices.

A highly sensitive chemosensor for rapid recognition of Cu and HSO in 100% aqueous solution.

Dual-responsive chemosensors have garnered much research interests owing to the ability of recognizing two analytes simultaneously. Herein, the chemosensor BPIS composed of hemicyanine and 2, 2'-dipyridylamine (DPA) was facilely synthesized for sensitive and expeditious recognition of Cu and HSO in 100% aqueous solution. By adding Cu, BPIS showed substantial spectral changes accompanied by a noticeable color change from pink to yellow under daylight.