Wearable and interactive multicolored photochromic fiber display

Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent,wearable interactive devices.However,significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight hardware.Here,we introduce a mass-produced,wearable,and interactive photochromic fiber that provides uniform multicolored light control.We designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the fiber.The impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission direction.In addition,we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single fiber.We prepared fibers on mass-produced kilometer-long using the thermal drawing method.The fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as needed.This work provides a new perspective and inspiration to the existing field of fiber display interaction,paving the way for future human–machine integration.

Design, Implementation and Control of an Amphibious Spherical Robot

We proposed and implemented a leg-vector water-jet actuated spherical robot and an underwater adaptive motion control system so that the proposed robot could perform exploration tasks in complex environments.Our aim was to improve the kinematic performance of spherical robots.We developed mechanical and dynamic models so that we could analyze the motions of the robot on land and in water.The robot was equipped with an Inertial Measurement Unit(IMU)that provided inclination and motion information.We designed three types of walking gait for the robot,with different stabilities and speeds.Furthermore,we proposed an online adjustment mechanism to adjust the gaits so that the robot could climb up slopes in a stable manner.As the system function changed continuously as the robot moved underwater,we implemented an online motion recognition system with a forgetting factor least squares algorithm.We proposed a generalized prediction control algorithm to achieve robust underwater motion control.To ensure real-time performance and reduce power consumption,the robot motion control system was implemented on a Zynq-7000 System-on-Chip(SoC).Our experimental results show that the robot’s motion remains stable at different speeds in a variety of amphibious environments,which meets the requirements for applications in a range of terrains.

Visual Detection of COVID‑19 from Materials Aspect

In the recent COVID-19 pandemic,World Health Organization emphasized that early detection is an effective strategy to reduce the spread of SARS-CoV-2 viruses.Several diagnostic methods,such as reverse transcription-polymerase chain reaction(RT-PCR)and lateral flow immunoassay(LFIA),have been applied based on the mechanism of specific recognition and binding of the probes to viruses or viral antigens.Although the remarkable progress,these methods still suffer from inadequate cellular materials or errors in the detection and sampling procedure of nasopharyngeal/oropharyngeal swab collection.Therefore,developing accurate,ultrafast,and visualized detection calls for more advanced materials and technology urgently to fight against the epidemic.In this review,we first summarize the current methodologies for SARS-CoV-2 diagnosis.Then,recent representative examples are introduced based on various output signals(e.g.,colorimetric,fluorometric,electronic,acoustic).Finally,we discuss the limitations of the methods and provide our perspectives on priorities for future test development.

Making Passive Daytime Radiative Cooling Metafabrics on a Large Scale

Cooling is pervasive in modern society and contributes signifcantly to global energy use.A hierarchical-morphology metafabric has been recently reported to show efcient passive daytime radiative cooling ability and may be also easily scaled up by industrial textile manufacturing technology.The above study represents an important advance in personal thermal management through the use of intelligent garments.

Fiber Electronics Bring a New Generation of Acoustic Fabrics

Acoustic fabrics have traditionally been served as sound absorbers,dissipating sound signals into unusable heat.An innovative electronic fber woven into an elaborately designed fabric enables an unprecedented acoustic fabric.Demonstrated useful applications in sound direction identifcation,acoustic communications and heart sound auscultation illustrate a wide spectrum of unforeseen opportunities of the considerable technology.

Nanoprocessed Silk Makes Skin Feel Cool

Silk is one of the most favourable fabrics with cooling sensation.The nanoprocessing of silk through a molecular bonding design and scalable coupling reagent-assisted method can achieve subambient daytime radiative cooling,opening up a new pathway to realizing thermoregulatory materials for sustainable energy.

Correction: Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning

Following publication of the original article[1],the authors reported an error in the Funding number.The current Funding section is as below:This work was supported by the National Key R&D Program of China(No.2020YFA040070),the National Natural Science Foundation of China(grant No.11721202),the Program of State Key Laboratory of Marine Equipment(No.SKLMEA-K201910).

Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning

Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.In general,approximate solutions can be obtained by iterative optimization methods.In the current work,a practical particle reconstruction method based on a convolutional neural network(CNN)with geometry-informed features is proposed.The proposed technique can refine the particle reconstruction from a very coarse initial guess of particle distribution that is generated by any traditional algebraic reconstruction technique(ART)based methods.Compared with available ART-based algorithms,the novel technique makes significant improvements in terms of reconstruction quality,robustness to noise,and at least an order of magnitude faster in the offline stage.