Giant low-field reversible magnetocaloric effect at liquid helium temperature of niobium and iron co-substituted EuTiO_(3) compounds

Giant magnetocaloric effect(MCE)materials in the liquid helium temperature region have attracted a lot of attention in the field of low-temperature magnetic refrigeration(MR).In this study,a series of niobium(Nb)and iron(Fe)co-substituted EuTiO_(3) perovskites with cubic structure(space group pm3m)was successfully fabricated,and their magnetic properties as well as cryogenic magnetocaloric effects were investigated in detail.As expected,the introduction of Nb and Fe can significantly modulate the magnetic phase transition and magnetocaloric effect of the EuTiO_(3) compounds.With increasing Fe concentration,two local minima corresponding to the AFM-FM magnetic phase transition near 5.0 K and FM-PM transition near 10 K with no hysteresis in the thermomagnetic curves are observed,which is attributed to an enhancement of FM coupling.At the same time,the gradually widened-ΔSM-T curves and the two peaks with a broad shoulder lead to considerable refrigeration capacity(RC).With the field change ofΔH=2 T,the calculated values of-ΔS_(M)^(max) for the EuTi_(0.9375-x)Nb_(0.0625)Fe_(x)O_(3)(x=0.075,0.1,0.125,0.15)compounds are 24.2,17.6,14.5 and 14.0 J/(kg·K),respectively.The corresponding RC values were calculated to be 144.6,138.3,151.2 and 159 J/kg,respectively.Especially,the values of-ΔS_(M)^(max) for EuTi_(0.8625)Nb_(0.0625)Fe_(0.075)O_(3) are 8.6 and 15.1 J/(kg·K)under low field changes of 0.5 and 1 T,respectively.The giant low-field reversible magnetocaloric effect makes them attractive candidates for magnetic refrigeration in the liquid helium temperature region.

Magnetic properties and magnetocaloric effects in Eu(Ti,Nb,Mn)O_(3) perovskites

In perovskite EuTiO_(3),the magnetic characteristics and magnetocaloric effect(MCE) can be flexibly regulated by converting the magnetism from antiferromagnetic to ferromagnetic.In the present work,a series of Eu(Ti,Nb,Mn)O_(3) compounds,abbreviated as ETNMO for convenience of description,was fabricated and their crystallography,magnetism together with cryogenic magnetocaloric effects were systematically investigated.The crystallographic results demonstrate the cubic perovskite structure for all the compounds,with the space group of Pm3m.Two magnetic phase transitions are observed in these second-order phase transition(SOPT) materials.The joint substitution of elements Mn and Nb can considerably manipulate the magnetic phase transition process and magnetocaloric performance of the ETNMO compounds.As the Mn content increases,gradually widened-ΔS_(M)-T curves are obtained,and two peaks with a broad shoulder are observed in the-ΔS_(M)-T curves for Δμ_(0)H≤0-1 T.Under a field change of 0-5 T,the values of maximum magnetic entropy change(-ΔS_(M)^(max)) and refrigeration capacity(RC) are evaluated to be 34.7 J/(kg·K) and 364.9 J/kg for EuTi_(0.8625)Nb_(0.0625)Mn_(0.075)O_(3), 27.8 J/(kg·K) and367.6 J/kg for EuTi_(0.8375)Nb_(0.0625)Mn_(0.1)O_(3),23.2 J/(kg·K) and 369.2 J/kg for EuTi_(0.8125)Nb_(0.0625)Mn_(0.125)O_(3),17.1 J/(kg·K) and 357.6 J/kg for EuTi_(0.7875)Nb_(0.0625)Mn_(0.15)O_(3),respectively.The co nsiderable MCE parameters make the ETNMO compounds potential candidates for cryogenic magnetic refrigeration.

Recent advances in constructing new type of epoxy resin flame retardant system using ammonium polyphosphate

In recent years,research has focused heavily on the investigation of functionalized ammonium polyphosphate(APP)flame retardants to improve the fire safety of epoxy resins(EP).The reason for this is the dual nature of APP's performance in fire protection of EP.This article provides a comprehensive overview of the advances in the use of functionalized APP flame retardants to improve the fire resistance of EP materials.It then presents the improvement of the modification of the functionalized APP flame retardants in terms of the hydrophobicity,compatibility and catalytic ability of the flame retardants,as well as the effects on the fire resistance,heat resistance,smoke reduction and mechanical properties of the EP composites.After the summary and comparison of the relevant studies,it is clear that the functionalized APP flame retardants can effectively improve the fire safety of EP composites and offset the adverse effects of APP in EP flame retardant applications.In addition,APP flame retardants can obtain various excellent functions through the use of materials with different properties,and the interaction between APP and materials can also lead to more efficient fire protection.However,the current problem is to find ways to streamline the process and minimise the costs associated with functionalized APP flame retardants,as well as to use them effectively in industrial production.We hope that this review can provide valuable hints and insights for the practical application of functionalized APP in EP and perspectives for future research.

A model for aircraft cabin evacuation considering passenger type

Pedestrian safety evacuation in aircraft cabins has been a challenging problem because of the aircraft’s unique characteristics,such as the diversity of passengers and the restricted evacuation environment.It is difficult to reproduce evacuation activities in aircraft cabin due to safety concerns and cost constraints.To fill this gap,an improved cellular automaton model of crowd evacuation for aircraft cabin is established by incorporating the characteristics of cabin space structures and passenger attributes.Passengers are divided into healthy individual passengers and disabled-healthy group passengers,whose movement mechanisms are quantified.Based on the constructed model,simulation experiments are conducted using the configuration cabin layout of B737-800 as an example.The results show that the evacuation time is prolonged with increased passenger density and the number of disabled passengers.Moreover,the overall evacuation time is insignificantly affected by whether disabled-healthy group passengers’seats are close to the aisle or window,and the evacuation efficiency is best when their seats are evenly distributed in the cabin.The evacuation time is the shortest when all cabin doors are open,and pedestrians are evacuated the slowest when the central emergency doors are closed.This study pro-vides valuable insights into effective strategies for pedestrian evacuation and crowd emergency management of civil aircraft.

Early fire detection technology based on improved transformers in aircraft cargo compartments

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light.This often results in a high false alarm rate in complex air transportation envi-ronments.The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information.This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model.Dual-wavelength optical sensors,flue gas analyzers,and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy.The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective,which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN(recurrent neural network)and CNN(convolutional neural network).Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information,respectively,which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism.Finally,the output results of the two models are fused through the gate mechanism.The research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire information.Compared with the traditional deep learning model,the multivariate fire pre-diction model constructed by the improved Transformer can better detect fires,and the accuracy rate is 0.995.

Review of prevention and mitigation technologies for thermal runaway in lithium-ion batteries

Lithium-ion batteries have been widely used in transportation,power equipment,aerospace,and other fields.However,the complex electrochemical reactions inside the battery cause excessive heat generation rate due to thermal,mechanical,and electrical abuse conditions,and even lead to thermal runaway.The problem of thermal runaway has become an important factor limiting its use.This review summarizes the intrinsic safety of batteries,thermal management,early monitoring and warning for thermal runaway,fire prevention and fire suppression technologies.The intrinsic safety technologies were summarized from the aspects of electrolyte flame retardancy,improvement of thermal stability of battery materials,and ceramic separators.To effectively control battery temperature,thermal management technologies were elaborated from the perspectives of air cooling,liquid cooling,heat pipes,phase change materials,and coupled thermal management.Single parameter detection,multi parameter composite detection,and new detection technologies were also discussed.In-situ monitoring of batteries based on fiber optic sensors helps to achieve early warning of thermal runaway.After thermal runaway occurs,fire prevention and fire extinguishing technology can effectively reduce the harm of thermal runaway,which should be given sufficient attention.This work provides important references value and research ideas for the prevention and mitigation of thermal runaway in lithium-ion batteries.

Early smoke and flame detection based on transformer

Fire-detection technology plays a critical role in ensuring public safety and facilitating the development of smart cities.Early fire detection is imperative to mitigate potential hazards and minimize associated losses.However,existing vision-based fire-detection methods exhibit limited generalizability and fail to adequately consider the effect of fire object size on detection accuracy.To address this issue,in this study a decoder-free fully transformer-based(DFFT)detector is used to achieve early smoke and flame detection,improving the detection performance for fires of different sizes.This method effectively captures multi-level and multi-scale fire features with rich semantic information while using two powerful encoders to maintain the accuracy of the single-feature map prediction.First,data augmentation is performed to enhance the generalizability of the model.Second,the detection-oriented transformer(DOT)backbone network is treated as a single-layer fire-feature extractor to obtain fire-related features on four scales,which are then fed into an encoder-only single-layer dense prediction module.Finally,the prediction module aggregates the multi-scale fire features into a single feature map using a scale-aggregated encoder(SAE).The prediction module then aligns the classification and regression features using a task-aligned encoder(TAE)to ensure the semantic interaction of the classification and regression predictions.Experimental results on one private dataset and one public dataset demonstrate that the adopted DFFT possesses high detection accuracy and a strong generalizability for fires of different sizes,particularly early small fires.The DFFT achieved mean average precision(mAP)values of 87.40%and 81.12%for the two datasets,outperforming other baseline models.It exhibits a better detection performance on flame objects than on smoke objects because of the prominence of flame features.

Cerium oxide nanozymes alleviate oxidative stress in tenocytes for Achilles tendinopathy healing

Background:Reactive oxygen species(ROS)is considered as ubiquitous and highly active chemicals that influence tendon integrity and orchestrate tendon repair.With significant recent advances in nanomaterials,cerium oxide nanoparticles(CeO_(2)NPs)exhibit superoxide dismutase-and catalase-like activities.Herein,we introduced a therapeutic approach of CeO_(2)NPs for Achilles tendinopathy(AT)healing.Methods:CeO_(2)NPs were synthesized to examine their effect as ROS scavengers on AT healing in vitro and in vivo.The mRNA levels of inflammatory factors were evaluated in AT after CeO_(2)NPs treatment in vitro.The mechanisms underlying CeO_(2)NPs-mediated stimulation of NRF2 translocation and ERK signaling were verified through immunofluorescence and Western blot analysis.The efficacy of CeO_(2)NPs was tested in an AT rat model in comparison with the control.Results:CeO_(2)NPs not only significantly scavenged multiple ROS and suppressed ROS-induced inflammatory reactions but also protected cell proliferation under oxidative stress induced by tert-butyl hydroperoxide(TBHP).Moreover,CeO_(2)NPs could promote NRF_(2)nuclear translocation for anti-oxidation and anti-inflammation through the ERK signaling pathway.In a rat model of collagenase-induced tendon injuries,CeO_(2)NPs showed significant therapeutic efficacy by ameliorating tendon damage.Conclusion:The present study provides valuable insights into the molecular mechanism of CeO_(2)NPs to ameliorate ROS in tenocytes via the ERK/NRF_(2)signaling pathway,which underscores the potential of CeO_(2)NPs for application in the treatment of enthesopathy healing.