Nonlinear charge transport induced by gate voltage oscillation in few-layer MnBi2Te4

Nonlinear charge transport, including nonreciprocal longitudinal resistance and nonlinear Hall effect, has garnered significant attention due to its ability to explore inherent symmetries and topological properties of novel materials. An exciting recent progress along this direction is the discovery of significant nonreciprocal longitudinal resistance and nonlinear Hall effect in the intrinsic magnetic topological insulator MnBi2Te4 induced by the quantum metric dipole. Given the importance of this finding, the inconsistent response with charge density, and conflicting requirement of C3z symmetry, it is imperative to elucidate every detail that may impact the nonlinear transport measurement. In this study, we reveal an intriguing experimental factor that inevitably gives rise to sizable nonlinear transport signal in MnBi2Te4. We demonstrate that this effect stems from the gate voltage oscillation caused by the application of a large alternating current to the sample. Furthermore, we propose a methodology to significantly suppress this effect by individually grounding the voltage electrodes during the second-harmonic measurements. Our investigation emphasizes the critical importance of thoroughly assessing the impact of gate voltage oscillation before determining the intrinsic nature of nonlinear transport in all 2D material devices with an electrically connected operative gate electrode.Comment: 28 pages, 12 figure

Helical Luttinger liquid on the edge of a 2-dimensional topological antiferromagnet

Boundary helical Luttinger liquid (HLL) with broken bulk time-reversal symmetry belongs to a unique topological class which may occur in antiferromagnets (AFM). Here, we search for signatures of HLL on the edge of a recently discovered topological AFM, MnBi2Te4 even-layer. Using scanning superconducting quantum interference device, we directly image helical edge current in the AFM ground state appearing at its charge neutral point. Such helical edge state accompanies an insulating bulk which is topologically distinct from the ferromagnetic Chern insulator phase as revealed in a magnetic field driven quantum phase transition. The edge conductance of the AFM order follows a power-law as a function of temperature and source-drain bias which serves as strong evidence for HLL. Such HLL scaling is robust at finite fields below the quantum critical point. The observed HLL in a layered AFM semiconductor represents a highly tunable topological matter compatible with future spintronics and quantum computation

Two types of zero Hall phenomena in few-layer MnBi2_2Te4_4

The van der Waals antiferromagnetic topological insulator MnBi$_2$Te$_4$ represents a promising platform for exploring the layer-dependent magnetism and topological states of matter. Despite the realization of several quantized phenomena, such as the quantum anomalous Hall effect and the axion insulator state, the recently observed discrepancies between magnetic and transport properties have aroused controversies concerning the topological nature of MnBi$_2$Te$_4$ in the ground state. Here, we demonstrate the existence of two distinct types of zero Hall phenomena in few-layer MnBi$_2$Te$_4$. In addition to the robust zero Hall plateau associated with the axion insulator state, an unexpected zero Hall phenomenon also occurs in some odd-number-septuple layer devices. Importantly, a statistical survey of the optical contrast in more than 200 MnBi$_2$Te$_4$ reveals that such accidental zero Hall phenomenon arises from the reduction of effective thickness during fabrication process, a factor that was rarely noticed in previous studies of 2D materials. Our finding not only resolves the controversies on the relation between magnetism and anomalous Hall effect in MnBi$_2$Te$_4$, but also highlights the critical issues concerning the fabrication and characterization of devices based on 2D materials.Comment: 21 pages, 4 figure

Demographic and Lifestyle Characteristics, but Not Apolipoprotein E Genotype, Are Associated with Intelligence among Young Chinese College Students

<div><p>Background</p><p>Intelligence is an important human feature that strongly affects many life outcomes, including health, life-span, income, educational and occupational attainments. People at all ages differ in their intelligence but the origins of these differences are much debated. A variety of environmental and genetic factors have been reported to be associated with individual intelligence, yet their nature and contribution to intelligence differences have been controversial.</p><p>Objective</p><p>To investigate the contribution of apolipoprotein E (<i>APOE</i>) genotype, which is associated with the risk for Alzheimer’s disease, as well as demographic and lifestyle characteristics, to the variation in intelligence.</p><p>Methods</p><p>A total of 607 Chinese college students aged 18 to 25 years old were included in this prospective observational study. The Chinese revision of Wechsler Adult Intelligence Scale (the fourth edition, short version) was used to determine the intelligence level of participants. Demographic and lifestyle characteristics data were obtained from self-administered questionnaires.</p><p>Results</p><p>No significant association was found between <i>APOE</i> polymorphic alleles and different intelligence quotient (IQ) measures. Interestingly, a portion of demographic and lifestyle characteristics, including age, smoking and sleep quality were significantly associated with different IQ measures.</p><p>Conclusions</p><p>Our findings indicate that demographic features and lifestyle characteristics, but not <i>APOE</i> genotype, are associated with intelligence measures among young Chinese college students. Thus, although <i>APOE</i> ε4 allele is a strong genetic risk factor for Alzheimer’s disease, it does not seem to impact intelligence at young ages.</p></div

Associations of subject demographic and lifestyle characteristics with IQ score measures (PSI and PRI) from multivariable analysis.

<p>Regression coefficients, 95% CIs, and p values were calculated from multivariable linear regression models. Regression coefficients are interpreted as the change in the mean IQ measure corresponding to the increase specified in parenthesis (continuous variables) or presence of the given characteristic (categorical variables). For all variables except height, weight, and BMI, models were adjusted for age, gender, height, weight, BMI, personality, smoking, alcohol consumption, physical exercise, and sleep quality. For height, weight, and BMI, models were adjusted for age, gender, personality, smoking, alcohol consumption, physical exercise, and sleep quality. P values of 0.005 or lower were considered as statistically significant after applying a Bonferroni correction for multiple testing. CI = confidence interval.</p><p>Associations of subject demographic and lifestyle characteristics with IQ score measures (PSI and PRI) from multivariable analysis.</p

Associations between <i>APOE</i> ε4 and IQ score measures.

<p>Regression coefficients, 95% CIs, and p values were calculated from linear regression models. Regression coefficients are interpreted as the difference in means of the given IQ measure between carriers and non-carriers of the ε4 allele (i.e. ε4 allele present vs. ε4 allele not present). A regression coefficient greater than “0” indicates a higher value of the given measure for ε4 carriers, and a regression coefficient less than “0” indicates a lower value of the given measure for ε4 carriers. Multivariable models were adjusted for school (Xiamen University or AnFang College), age, gender, height, weight, BMI, personality, smoking, drinking alcohol, physical exercise, and sleep quality. P values of 0.01 or lower were considered as statistically significant after applying a Bonferroni correction for multiple testing. Min = Minimum; Q1 = first quartile; Q3 = third quartile; Max = Maximum; CI = confidence interval.</p><p>Associations between <i>APOE</i> ε4 and IQ score measures.</p

Associations between <i>APOE</i> ε2 and IQ score measures.

<p>Regression coefficients, 95% CIs, and p values were calculated from linear regression models. Regression coefficients are interpreted as the difference in means of the given IQ measure between carriers and non-carriers of the ε2 allele (i.e. ε2 allele present vs. ε2 allele not present). A regression coefficient greater than “0” indicates a higher value of the given measure for ε2 carriers, and a regression coefficient less than “0” indicates a lower value of the given measure for ε2 carriers. Multivariable models were adjusted for school (Xiamen University or AnFang College), age, gender, height, weight, BMI, personality, smoking, drinking alcohol, physical exercise, and sleep quality. P values of 0.01 or lower were considered as statistically significant after applying a Bonferroni correction for multiple testing. Min = Minimum; Q1 = first quartile; Q3 = third quartile; Max = Maximum; CI = confidence interval.</p><p>Associations between <i>APOE</i> ε2 and IQ score measures.</p

Associations of subject demographic and lifestyle characteristics with IQ score measures (Full Scale IQ Score, VCI, and WMI) from multivariable analysis.

<p>Regression coefficients, 95% CIs, and p values were calculated from multivariable linear regression models. Regression coefficients are interpreted as the change in the mean IQ measure corresponding to the increase specified in parenthesis (continuous variables) or presence of the given characteristic (categorical variables). For all variables except height, weight, and BMI, models were adjusted for age, gender, height, weight, BMI, personality, smoking, alcohol consumption, physical exercise, and sleep quality. For height, weight, and BMI, models were adjusted for age, gender, personality, smoking, alcohol consumption, physical exercise, and sleep quality. P values of 0.005 or lower were considered as statistically significant after applying a Bonferroni correction for multiple testing. CI = confidence interval.</p><p>Associations of subject demographic and lifestyle characteristics with IQ score measures (Full Scale IQ Score, VCI, and WMI) from multivariable analysis.</p