Optimizing Room-Temperature Thermoelectric Performance of n‑Type Bi₂Te_2.7Se_0.3

Bi2Te3-based compounds are exclusive commercial thermoelectric materials around room temperature. For n-type compounds, optimal thermoelectric properties are normally obtained at temperatures higher than room temperature to suppress the bipolar effect through increased carrier concentration. We find that doping with trace amounts of Cd and the addition of excess Bi are effective ways to optimize carrier concentration and achieve enhanced room-temperature thermoelectric performance for the Bi2Te2.7Se0.3 alloy in this work. For the Cd-doped samples, the replacement of Cd with Bi leads to not only a significant decrease in electron concentration but also apparently reduces the total thermal conductivity. The addition of excess Bi in the samples creates a Bi-rich synthetic atmosphere during the synthesis process, leading to increased BiTe antisite defects, decreased electron concentration, and reduced total thermal conductivity. Doping a small amount of Cd or adding excess Bi causes optimal thermoelectric performance of the n-type Bi2Te2.7Se0.3 sample shifts obviously toward low temperatures, and the samples with 0.4 atom % Cd and 0.8 atom % excess Bi achieve maximum zT of ∼0.97 at 448 K and ∼0.88 at 348 K, respectively

Student characteristics and IQ information.

<p>Continuous variables are summarized with the sample median (minimum, first quartile, third quartile, maximum). Information was unavailable in some individuals regarding height (N = 32), weight (N = 35), BMI (N = 35), personality (N = 39), smoking (N = 32), drinking (N = 32), physical exercise (N = 32), and sleep quality (N = 33).</p><p>Student characteristics and IQ information.</p

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