Mechanism and Interface Study of One-to-one Metal NP/Metal Organic Framework Core-shell Structure

Thesis advisor: Chia-Kuang (Frank) TsungThe core-shell hybrid structure is the simplest motif of two-component systems which consists of an inner core coated by an outer shell. Core-shell composite materials are attractive for their biomedical, electronic and catalytic applications in which interface between core and shell is critical for various functionalities. However, it is still challenging to study the exact role that interface plays during the formation of the core-shell structures and in the properties of the resulted materials. By studying the formation mechanism of a well interface controlled one-to-one metal nanoparticle (NP)@zeolite imidazolate framework-8 (ZIF-8) core-shell material, we found that the dissociation of capping agents on the NP surface results in direct contact between NP and ZIF-8, which is essential for the formation of core-shell structure. And the amount of capping agents on the NP surface has a significant effect to the crystallinity and stability of ZIF-8 coating shell. Guided by our understanding to the interface, one-to-one NP@UiO-66 core-shell structure has also been achieved for the first time. We believe that our research will help the development of rational design and synthesis of core-shell structures, particularly in those requiring good interface controls.Thesis (MS) — Boston College, 2017.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Chemistry

Specify Robust Causal Representation from Mixed Observations

Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models. In this paper, we develop a learning method to learn such representation from observational data by regularizing the learning procedure with mutual information measures, according to the hypothetical factored causal graph. We theoretically and empirically show that the models trained with the learned causal representations are more robust under adversarial attacks and distribution shifts compared with baselines. The supplementary materials are available at https://github.com/ymy $4323460 / \mathrm{CaRI} /$.Comment: arXiv admin note: substantial text overlap with arXiv:2202.0838

Leveraging Historical Medical Records as a Proxy via Multimodal Modeling and Visualization to Enrich Medical Diagnostic Learning

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.Comment: Accepted by IEEE VIS 202

Learning to Select Cuts for Efficient Mixed-Integer Programming

Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in the early stage, thus largely reducing the computational burden without hurting the solution accuracy. However, the major cut selection approaches heavily rely on heuristics, which strongly depend on the specific problem at hand and thus limit their generalization capability. In this paper, we propose a data-driven and generalizable cut selection approach, named Cut Ranking, in the settings of multiple instance learning. To measure the quality of the candidate cuts, a scoring function, which takes the instance-specific cut features as inputs, is trained and applied in cut ranking and selection. In order to evaluate our method, we conduct extensive experiments on both synthetic datasets and real-world datasets. Compared with commonly used heuristics for cut selection, the learning-based policy has shown to be more effective, and is capable of generalizing over multiple problems with different properties. Cut Ranking has been deployed in an industrial solver for large-scale MIPs. In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12.42% over the production solver without any accuracy loss of solution.Comment: Paper accepted at Pattern Recognition journa

Efficient organic solar cells enabled by simple non-fused electron donors with low synthetic complexity

Abstract Fused‐ring electron donors boost the efficiency of organic solar cells (OSCs), but they suffer from high cost and low yield for their large synthetic complexity (SC > 30%). Herein, the authors develop a series of simple non‐fused‐ring electron donors, PF1 and PF2, which alternately consist of furan‐3‐carboxylate and 2,2′‐bithiophene. Note that PF1 and PF2 present very small SC of 9.7% for their inexpensive raw materials, facile synthesis, and high synthetic yield. Compared to their all‐thiophene‐backbone counterpart PT‐E, two new polymers feature larger conjugated plane, resulting in higher hole mobility for them, especially a value up to ≈10 −4 cm 2 V −1 ·s for PF2 with longer alkyl side chain. Meanwhile, PF1 and PF2 exhibit larger dielectric constant and deeper electronic energy level versus PT‐E. Benefiting from the better physicochemical properties, the efficiencies of PF1‐ and PF2‐based devices are improved by ≈16.7% and ≈71.3% relative to that PT‐E‐based devices, respectively. Furthermore, the optimized PF2‐based devices with introducing PC 71 BM as the third component deliver a higher efficiency of 12.40%. The work not only indicates that furan‐3‐carboxylate is a simple yet efficient building block for constructing non‐fused‐ring polymers but also provides a promising electron donor PF2 for the low‐cost production of OSCs.A simple structure non‐fused‐ring electron donor PF2 alternately consisting of furan‐3‐carboxylate and 2,2′‐bithiophene presents very small synthetic complexity of 9.7% as well as low material cost of ≈19.0 $ g −1 . More importantly, PF2 delivers a high efficiency of 12.4% coupled with strong operational stability. imag

TELEX HEBDOMADAIRE NR 95 DU 17.09.82 DESTINE A L'ENSEMBLE DES DELEGATIONS EXTERIEURES ET BUREAUX DE PRESS ET D'INFORMATION INDEPENDANTS DANS LES PAYS TIERS = WEEKLY MEMO NO. 95 FOR 17.09.82 TO FOREIGN DELEGATIONS AND PRESS BUREAUS OF THIRD COUNTRIES

<p>High-performance liquid chromatography (HPLC) results of (A) commercial surfactin sample, and (B) our extract surfactin of <i>B</i>. <i>subtilis</i> HH2 in LB medium. There were three main peaks (Peak A-C) of the extract and the surfactin standard in the same location.</p

The chirally improved quark propagator and restoration of chiral symmetry

The chirally improved (CI) quark propagator in Landau gauge is calculated in two flavor lattice Quantum Chromodynamics. Its wave-function renormalization function $Z(p^2)$ and mass function $M(p^2)$ are studied. To minimize lattice artifacts, tree-level improvement of the propagator and tree-level correction of the lattice dressing functions is applied. Subsequently the CI quark propagator under Dirac operator low-mode removal is investigated. The dynamically generated mass in the infrared domain of the mass function is found to dissolve continuously as a function of the reduction level and strong suppression of $Z(p^2)$ for small momenta is observed.Comment: 9 pages, 8 figures; accepted at Phys. Lett.