Bandgap engineering in semiconductor alloy nanomaterials with widely tunable compositions

Over the past decade, tremendous progress has been achieved in the development of nanoscale semiconductor materials with a wide range of bandgaps by alloying different individual semiconductors. These materials include traditional II-VI and III-V semiconductors and their alloys, inorganic and hybrid perovskites, and the newly emerging 2D materials. One important common feature of these materials is that their nanoscale dimensions result in a large tolerance to lattice mismatches within a monolithic structure of varying composition or between the substrate and target material, which enables us to achieve almost arbitrary control of the variation of the alloy composition. As a result, the bandgaps of these alloys can be widely tuned without the detrimental defects that are often unavoidable in bulk materials, which have a much more limited tolerance to lattice mismatches. This class of nanomaterials could have a far-reaching impact on a wide range of photonic applications, including tunable lasers, solid-state lighting, artificial photosynthesis and new solar cells

Carrier Sense Random Packet CDMA Protocol in Dual-Channel Networks

Code resource wastage is caused by the reason that many hopping frequency (FH) sequences are unused, which occurs under the condition that the number of the actual subnets needed for the tactical network is far smaller than the networking capacity of code division net¬working. Dual-channel network (DCN), consisting of one single control channel and multiple data channels, can solve the code resource wastage effectively. To improve the anti-jamming capability of the control channel of DCN, code division multiple access (CDMA) technology was introduced, and a carrier sense random packet (CSRP) CDMA protocol based on random packet CDMA (RP-CDMA) was proposed. In CSRP-CDMA, we provide a carrier sensing random packet mechanism and a packet-segment acknowledgement policy. Furthermore, an analytical model was developed to evaluate the performance of CSRP-CDMA networks. In this model, the impacts of multi-access interference from both inter-clusters and intra-clusters were analyzed, and the mathematical expressions of packet transmission success probability, normalized network throughput and signal interference to noise ratio, were also derived. Analytical and simulation results demonstrate that the normalized network throughput of CSRP-CDMA outperforms traditional RP-CDMA by 10%, which can guarantee the resource utilization efficiency of the control channel in DCNs

Linking engagement and performance: The social network analysis perspective

Theories developed by Tinto and Nora identify academic performance, learning gains, and involvement in learning communities as significant facets of student engagement that, in turn, support student persistence. Collaborative learning environments, such as those employed in the Modeling Instruction introductory physics course, provide structure for student engagement by encouraging peer-to-peer interactions. Because of the inherently social nature of collaborative learning, we examine student interactions in the classroom using network analysis. We use centrality---a family of measures that quantify how connected or "central" a particular student is within the classroom network---to study student engagement longitudinally. Bootstrapped linear regression modeling shows that students' centrality predicts future academic performance over and above prior GPA for three out of four centrality measures tested. In particular, we find that closeness centrality explains 28 % more of the variance than prior GPA alone. These results confirm that student engagement in the classroom is critical to supporting academic performance. Furthermore, we find that this relationship for social interactions does not emerge until the second half of the semester, suggesting that classroom community develops over time in a meaningful way

The qq-log-convexity of Domb's polynomials

In this paper, we prove the $q$-log-convexity of Domb's polynomials, which was conjectured by Sun in the study of Ramanujan-Sato type series for powers of $\pi$. As a result, we obtain the log-convexity of Domb's numbers. Our proof is based on the $q$-log-convexity of Narayana polynomials of type $B$ and a criterion for determining $q$-log-convexity of self-reciprocal polynomials.Comment: arXiv admin note: substantial text overlap with arXiv:1308.273

On the qq-log-convexity conjecture of Sun

In his study of Ramanujan-Sato type series for $1/\pi$, Sun introduced a sequence of polynomials $S_n(q)$ as given by $$S_n(q)=\sum\limits_{k=0}^n{n\choose k}{2k\choose k}{2(n-k)\choose n-k}q^k,$$ and he conjectured that the polynomials $S_n(q)$ are $q$-log-convex. By imitating a result of Liu and Wang on generating new $q$-log-convex sequences of polynomials from old ones, we obtain a sufficient condition for determining the $q$-log-convexity of self-reciprocal polynomials. Based on this criterion, we then give an affirmative answer to Sun's conjecture

Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting

The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner’s guide to carrying out research using SNA, including how to develop data collection instruments, setup protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students’ physics anxiety and the social networks they participate in throughout the course of a semester. We find that students’ with greater numbers of outgoing interactions are more likely to experience decrease in anxiety even while we control for pre-anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for other researchers to use this approach in their work—the SNA toolbox—that can be accessed on GitHub

Lead halide perovskite nanowires stabilized by block copolymers for Langmuir-Blodgett assembly

The rapid development of solar cells based on lead halide perovskites (LHPs) has prompted very active research activities in other closely-related fields. Colloidal nanostructures of such materials display superior optoelectronic properties. Especially, one-dimensional (1D) LHPs nanowires show anisotropic optical properties when they are highly oriented. However, the ionic nature makes them very sensitive to external environment, limiting their large scale practical applications. Here, we introduce an amphiphilic block copolymer, polystyrene-block-poly(4-vinylpyridine) (PS-P4VP), to chemically modify the surface of colloidal CsPbBr3 nanowires. The resulting core-shell nanowires show enhanced photoluminescent emission and good colloidal stability against water. Taking advantage of the stability enhancement, we further applied a modified Langmuir-Blodgett technique to assemble monolayers of highly aligned nanowires, and studied their anisotropic optical properties. [Figure not available: see fulltext.]