The Critical Role of Fillers in Composite Polymer Electrolytes for Lithium Battery

With excellent energy densities and highly safe performance,solidstate lithium batteries(SSLBs)have been hailed as promising energy storage devices.Solid-state electrolyte is the core component of SSLBs and plays an essential role in the safety and electrochemical performance of the cells.Composite polymer electrolytes(CPEs)are considered as one of the most promising candidates among all solid-state electrolytes due to their excellent comprehensive performance.In this review,we briefly introduce the components of CPEs,such as the polymer matrix and the species of fillers,as well as the integration of fillers in the polymers.In particular,we focus on the two major obstacles that affect the development of CPEs:the low ionic conductivity of the electrolyte and high interfacial impedance.We provide insight into the factors influencing ionic conductivity,in terms of macroscopic and microscopic aspects,including the aggregated structure of the polymer,ion migration rate and carrier concentration.In addition,we also discuss the electrode-electrolyte interface and summarize methods for improving this interface.It is expected that this review will provide feasible solutions for modifying CPEs through further understanding of the ion conduction mechanism in CPEs and for improving the compatibility of the electrode-electrolyte interface.

Promote the conductivity of solid polymer electrolyte at room temperature by constructing a dual range ionic conduction path

Poly(ethylene oxide)(PEO)is a classic matrix model for solid polymer electrolyte which can not only dissociate lithium-ions(Li^(+)),but also can conduct Li^(+) through segmental motion in long-range.However,the crystal aggregation state of PEO restricts the conduction of Li^(+) especially at room temperature.In this work,an amorphous polymer electrolyte with ethylene oxide(EO)and propylene oxide(PO)block structure(B-PEG@DMC)synthesized by the transesterification is firstly obtained,showing an ionic conductivity value of 1.1×10^(5) S/cm at room temperature(25℃).According to the molecular dynamics(MD)simulation,the PO segments would lead to an inconsecutive and hampered conduction of Li^(+),which is not beneficial to the short range conduction of Li^(+).Thus the effect of transformation of aggregation state on the improveme nt of ionic conductivity is not eno ugh,it is n ecessary to further consider the differe nt coupled behaviours of EO and PO segments with Li^(+).In this way,we blend this amorphous polymer(B-PEG@DMC)with PEO to obtain a dual range ionic conductive solid polymer electrolyte(D-SPE)with further improved ionic conductivity promoted by constructing a dual range fast ionic conduction,which eventually shows a further improved ionic conductivity value of 2.3×10^(5) S/cm at room temperature.

Monolithic polyimine vitrimer/graphene aerogel composites

Graphene-polymer composites have attracted great attention as sensing materials due to their tailorable electrical conductivity, physicochemical properties, and sensitivity to geometric and functional changes.Herein, we report the first example of cylindrical monolithic polyimine vitrimer/graphene composites with excellent mechanical, compressive, rehealable and recyclable, and piezoresistive properties via simple infiltration of polymer monomers into the pores of graphene aerogel followed by thermal curing. The composites exhibit excellent durable compressibility(negligible reduction in the compression properties even after 3000 consecutive compression cycles), rapid recovery to the original size upon stress released,high compressive strength(up to 1.2 MPa), and high conductivity(up to 79 S/m). Excellent piezoresistive properties were observed, displaying consistent and reliable change of the electrical resistance with the compression ratio. Furthermore, rehealing with ~100% recovery of the compressive strength and electric conductivity was achieved under mild rehealing conditions, which is highly desired but has rarely been reported for electronic materials. The facile strategy for fabrication of rehealable monolithic polymer/GAs can open new possibilities for the sustainable development of composites with high electrical conductivity for various applications such as sensing, health monitoring, and movement detection.

Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

Phonon Boltzmann transport equation(BTE)is a key tool for modeling multiscale phonon transport,which is critical to the thermal management of miniaturized integrated circuits,but assumptions about the system temperatures(i.e.,small temperature gradients)are usually made to ensure that it is computationally tractable.To include the effects of large temperature non-equilibrium,we demonstrate a data-free deep learning scheme,physics-informed neural network(PINN),for solving stationary,mode-resolved phonon BTE with arbitrary temperature gradients.This scheme uses the temperature-dependent phonon relaxation times and learns the solutions in parameterized spaces with both length scale and temperature gradient treated as input variables.Numerical experiments suggest that the proposed PINN can accurately predict phonon transport(from 1D to 3D)under arbitrary temperature gradients.Moreover,the proposed scheme shows great promise in simulating device-level phonon heat conduction efficiently and can be potentially used for thermal design.

The critical need to establish standards for data quality in intelligent medicine

Medical artificial intelligence(AI)is an important technical asset to support medical supply-side reforms and national development in the big data era.Clinical data from multiple disciplines represent building blocks for the development and application of AI-aided diagnostic and treatment systems based on medical big data.However,the inconsistent quality of these data resources in AI research leads to waste and inefficiencies.Therefore,it is crucial that the field formulatesthe requirements and content related to data processing as part of the development of intelligent medicine.To promote medical AI research worldwide,the“Belt and Road”International Ophthalmic Artificial Intelligence Research and Development Alliance will establish a series of expert recommendations for data quality in intelligent medicine.

Expert recommendations on collection and annotation of otoscopy images for intelligent medicine

Middle and outer ear diseases are common otological diseases worldwide.Otoscopy and otoendoscopy exami-nations are essential first steps in the evaluation of patients with otological diseases.Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy,leading to delays in treatment or complications.Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future.However,the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems,and no standardized process for data acquisition,and annotation of otoscopy images in intelligent medicine has yet been fully established.The standards for data storage and data management are unified with those of other specialties and are introduced in detail here.This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine;it would thus lay a solid foundation for the standardized collection,storage,and annotation of oto-scopy images and the application of training algorithms,and promote the development of automatic diagnosis and treatment for otological diseases.The full text introduced image collection(including patient preparation,equipment standards,and image storage),image annotation standards,and quality control.

Expert recommendations on data collection and annotation of two dimensional ultrasound images in azoospermic males for evaluation of testicular spermatogenic function in intelligent medicine

Testicular two-dimensional ultrasound is a testing modality that is often used to evaluate azoospermia and other related diseases.With the continuous development of deep learning in recent years,the combination of deep learning and testicular ultrasound appears unstoppable despite a lack of relevant standards.One of the major problems associated with the digitization of ultrasound images is the uneven quality of data however,and a standardized data source and acquisition process has not yet been developed.Such a standard could fill the current gap,and establish acquisition criteria for ultrasound images of testes during the male reproductive period,including grayscale ultrasound,shear wave elastography,and contrast-enhanced ultrasound.By following these guidelines the quality of testicular ultrasound images would be improved and standardized,which would lay a solid foundation for the standardization of testicular ultrasound images,and assist automated evaluation of testicular spermatogenic function of whole testis in azoospermic males.

Highly Conductive Poly(Imide–Imine)Hybrid Vitrimer-Graphene Aerogel Composites

Comprehensive Summary,Although polyimides(PIs)have shown great potential for a broad range of applications,it remains very challenging to achieve the malleability,rehealability and recyclability for PIs and their composites targeting various applications,particularly for the rapidly emerging flexible and stretchable electronics.Herein,malleable conductive poly(imide-imine)hybrid(PIIH)vitrimer-graphene aerogel(GA)composites have been prepared,for the first time,via simple sol-gel film formation followed by heat-press.The resulting PIIH-GA composites exhibit not only the highly desired properties of thermosetting(strong mechanical strength)and thermoplastic(reprocessability)polymers,but also good conductivity enabled by the GA filler.PIIH3-GA-10(with 10 wt%GA)showed one of the highest electrical conductivities(26.7 S/m)for PI-based composites,as well as good electromagnetic interference(EMI)shielding performance.Moreover,the PIIH-GA films could maintain good performance during stretching and even after chemical recycling,which opens new opportunities for flexible and sustainable electronics development.

Recommendation on data collection and annotation of ocular appearance images in ptosis

Ptosis is a common ophthalmologic condition,and the diagnosis is primarily based on ocular appearance.Thediagnosis of such conditions can be improved using emerging technology such as artificial intelligence-basedmethods.However,unified data collection and labeling standards have not yet been established.This directlyimpacts the accuracy of ptosis diagnosis based on appearance alone.Therefore,in the present study,we aimedto establish a procedure to obtain and label images to devise a recommendation system for optimal recognitionof ptosis based on ocular appearances.This would help to standardize and facilitate data sharing and serve as aguideline for the development and improvisation of algorithms in artificial intelligence for ptosis.

Physics-informed neural networks for solving time-dependent mode-resolved phonon Boltzmann transport equation

The phonon Boltzmann transport equation(BTE)is a powerful tool for modeling and understanding micro-/nanoscale thermal transport in solids,where Fourier’s law can fail due to non-diffusive effect when the characteristic length/time is comparable to the phonon mean free path/relaxation time.However,numerically solving phonon BTE can be computationally costly due to its high dimensionality,especially when considering mode-resolved phonon properties and time dependency.In this work,we demonstrate the effectiveness of physics-informed neural networks(PINNs)in solving time-dependent mode-resolved phonon BTE.The PINNs are trained by minimizing the residual of the governing equations,and boundary/initial conditions to predict phonon energy distributions,without the need for any labeled training data.The results obtained using the PINN framework demonstrate excellent agreement with analytical and numerical solutions.Moreover,after offline training,the PINNs can be utilized for online evaluation of transient heat conduction,providing instantaneous results,such as temperature distribution.It is worth noting that the training can be carried out in a parametric setting,allowing the trained model to predict phonon transport in arbitrary values in the parameter space,such as the characteristic length.This efficient and accurate method makes it a promising tool for practical applications such as the thermal management design of microelectronics.

Circulating tumor DNA(ctDNA)-based minimal residual disease in non-small cell lung cancer

Lung cancer is the second most common cancer worldwide and the leading cause of cancer-related fatalities,with non-small cell lung cancer(NSCLC)accounting for 85%of all lung cancers.Over the past forty years,patients with NSCLC have had a 5-year survival rate of only 16%,despite improvements in chemotherapy,targeted therapy,and immunotherapy.Circulating tumor DNA(ctDNA)in blood can be used to identify minimal residual disease(MRD),and ctDNA-based MRD has been shown to be of significance in prognostic assessment,recurrence monitoring,risk of recurrence assessment,efficacy monitoring,and therapeutic intervention decisions in NSCLC.The level of MRD can be obtained by monitoring ctDNA to provide guidance for more precise and personalized treatment,the scientific feasibility of which could dramatically modify lung cancer treatment paradigm.In this review,we present a comprehensive review of MRD studies in NSCLC and focus on the application of ctDNA-based MRD in different stages of NSCLC in current clinical practice.

非天然氨基酸正交翻译技术:一种新型基因工程活病毒疫苗研发技术

非天然氨基酸正交翻译技术利用外源的非天然氨基酸氨酰tRNA合成酶(aaRS)基因和对应的tRNA基因构建非天然氨基酸正交翻译系统(Orthogonal translation system)。该正交翻译系统能利用终止密码子在蛋白翻译过程中将非天然氨基酸定点插入目标多肽链中。该技术不但是一种新的蛋白质生化研究工具,在新型基因工程病毒疫苗研究中更具有划时代的意义。利用人为构建的具有非天然氨基酸正交翻译系统的转基因细胞,通过在病毒复制的关键基因中引入提前终止密码子构建的突变病毒,在添加非天然氨基酸的情况下该基因仍能完整表达从而完成病毒的复制和传代,但该突变病毒在正常细胞(无非天然氨基酸正交翻译系统的宿主细胞)中因复制关键基因不能完整表达而无法复制传代,因而是一种复制缺陷型病毒。这种复制缺陷型病毒用作疫苗时兼具了减毒活疫苗免疫效果良好与灭活疫苗安全性高的优点,是一种较为理想的活病毒疫苗。文中简要综述了非天然氨基酸正交翻译技术在新型复制缺陷活病毒疫苗研究中的应用及其前景。