Effects of Rhizoma Acori Tatarinowii extracts on gamma-aminobutyric acid type A receptor alpha 1 subunit brain expression during development in a recurrent seizure rat model

Extracts from Rhizoma Acori Tatarinowii （Grassleaf Sweetflag Rhizome, Shichangpu） have been shown to improve learning and memory, reduce anxiety, allay excitement, and suppress seizures. Rhizoma Acori Tatarinowii extracts interact with y-aminobutyric acid and activate the y-aminobutyric acid type A receptor, although few studies have addressed the precise effects of v-aminobutyric acid type A receptor al subunit. In the present study, y-aminobutyric acid type A receptor al subunit protein expression in the cerebral cortex and hippocampus, and pathological scores of brain injury, were significantly greater following recurrent seizures, but significantly decreased following treatment with Rhizoma Acori Tatarinowii extracts. These results indicated that Rhizoma Acori Tatarinowii extracts down-regulated y-aminobutyric acid type A receptor al subunit protein expression in the cerebral cortex and hippocampus and protected seizure-induced brain injury during development.

Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning

The piezoelectric performance serves as the basis for the applications of piezoelectric ceramics.The ability to rapidly and accurately predict the piezoelectric coefficient(d_(33))is of much practical importance for exploring high-performance piezoelectric ceramics.In this work,a data-driven approach combining feature engineering,statistical learning,machine learning(ML),experimental design,and synthesis is trialed to investigate its accuracy in predicting d_(33) of potassium-sodium-niobate(K,Na)NbO_(3),KNN)-based ceramics.The atomic radius(AR),valence electron distance(DV)(Schubert),Martynov-Batsanov electronegativity(EN-MB),and absolute electronegativity(EN)are summarized as the four most representative features in describing d_(33) out of all 27 possible features for the piezoelectric ceramics.These four features contribute greatly to regression learning for predicting d_(33) and classification learning for distinguishing polymorphic phase boundary(PPB).The ML method developed in this work exhibits a high accuracy in predicting d_(33) of the piezoelectric ceramics.An example of KNN combined with 6 mo1%LiNbO_(3)demonstrates d_(33)3 of 184 pC/N,which is highly consistent with the predicted result.This work proposes a novel feature-oriented guideline for accelerating the design of piezoelectric ceramic systems with large d_(33),which is expected to be widely used in other functional materials.

Bandgap narrowing and polarization enhancement in(K,Na,Li)(Nb,Sb,Ta)O_(3)+x%Fe_(2)O_(3) lead-free ceramics for photovoltaic applications

The need for ferroelectric materials with both narrow bandgaps(Eg)and large remanent polarization(Pr)remains a key challenge to the development of high-efficiency ferroelectric photovoltaic(FPV)devices.In this work,[(K_(0.43)Na_(0.57))_(0.94)Li_(0.06)][(Nb_(0.94)Sb_(0.06))_(0.95)Ta_(0.05)]O_(3)(KNLNST)-based lead-free ceramics with narrow Eg and large P are obtained via Fe_(2)O_(3) doping.By optimizing the level of Fe_(2)O_(3) doping,a KNLNST+1.3%Fe_(2)O_(3) ceramic is fabricated that simultaneously possesses a narrow Eg of 1.74 eV and a large Pr of 27.05μC/cm^(2).These values are much superior to those of undoped KNLNST ceramics(Eg=3.1 eV and Pr=17.73μC/cm^(2)).While the large P stems from the increment of the volume ratio between the orthorhombic and tetragonal phases(Vo/VT)in KNLNST ceramics by proper amount of Fe3+doping,the narrow Eg is attributed to the coupling interaction between the Fe3+dopants and the B-site Sb3+host ions.Moreover,a switchable photovoltaic effect caused by the ferroelectric depolarization electric field(Edp)is observed in the KNLNST+1.3%Fe_(2)O_(3) ceramic-based device.Thanks to the narrower Eg and larger P,of the doped ceramic,the photovoltaic performance of the corresponding device(open-circuit voltage(Voc)=-5.28 V and short-circuit current density(JSC)=0.051μA/cm^(2))under a downward poling state is significantly superior to that of an undoped KNLNST-based device(Voc=-0.46 V and Jse=0.039μA/cm^(2)).This work offers a feasible approach to developing ferroelectric materials with narrow bandgaps and large Pr for photovoltaic applications.