Structural modifications to molecular systems that lead to the control of photon emission processes at the interfaces between photoactive materials play a key role in the development of fluorescence sensors, X-ray imaging scintillators, and organic light-emitting diodes (OLEDs). In this work, two donor–acceptor systems were used to explore and reveal the effects of slight changes in chemical structure on interfacial excited-state transfer processes. A thermally activated delayed fluorescence (TADF) molecule was chosen as the molecular acceptor. Meanwhile, two benzoselenadiazole-core MOF linker precursors, Ac-SDZ and SDZ, with the presence and absence of a C≡C bridge, respectively, were carefully chosen as energy and/or electron-donor moieties. We found that the SDZ -TADF donor–acceptor system exhibited efficient energy transfer, as evidenced by steady-state and time-resolved laser spectroscopy. Furthermore, our results demonstrated that the Ac-SDZ–TADF system exhibited both interfacial energy and electron transfer processes. Femtosecond-mid-IR (fs-mid-IR) transient absorption measurements revealed that the electron transfer process takes place on the picosecond timescale. Time-dependent density functional theory (TD-DFT) calculations confirmed that photoinduced electron transfer occurred in this system and demonstrated that it takes place from C≡C in Ac-SDZ to the central unit of the TADF molecule. This work provides a straightforward way to modulate and tune excited-state energy/charge transfer processes at donor–acceptor interfaces.

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.2c08844

Tunable Photoinduced Charge Transfer at the Interface between Benzoselenadiazole-Based MOF Linkers and Thermally Activated Delayed Fluorescence Chromophore

Online Analytical Processing (OLAP) is a crucial area of interest to several organizations in the previous decade. But these approaches are established as extremely useful to analyze and mine structured data, it can face problems in controlling text data. Usually, OLAP queries were ineffective for evaluating text data which are commonly compared with some contextual data but which suppose that examined for retrieving accurate outcomes. Usually, the OLAP applies to numeric data and there has been comparatively limited research in the domain of OLAP to text data. In this view, this article develops an Optimal Stacked Sparse Autoencoder based OLAP Hypercube Text Analysis (OSSAE-OLAP) technique. The presented OSSAE-OLAP technique focuses on the classification of appropriate data for text analysis. In the presented OSSAE-OLAP technique, the SSAE model is used for classification process which contains multiple stacks of sparse autoencoders. To enhance the performance of the SAE method, the pigeon inspired optimization (PIO) approach is utilized for the hyperparameter tuning process. For exhibiting the improved outcomes of the presented OSSAE-OLAP technique, a widespread experimental analysis is made and the outcomes revealed the supremacy of the OSSAE-OLAP technique.

Optimal Stacked Sparse Auto-encoder based Online Analytic Processing (OLAP) Hypercube Text Analysis

In this paper, we deal with the problem of similarity search about crowdedness for participatory-sensing buses for urban transportation. Similarity search is usually applied for measuring similarities in heterogeneous information networks. However, many models implement similarity search in a global setting, without taking object attributes into consideration. OCP, a novel OLAP-based crowdedness perception, is an attribute-enriched and meta-path-based model with machine learning to capture similarity based on the object connectivity, visibility and features. A set of common crowdedness attribute dimensions are defined across different types of objects, which can be obtained from the participatory passenger’s sensor data through deep-neural-network-based posture recognition. Accordingly, an object can be described as a series of node vectors from different dimensions. In such framework, OLAP is applied in analysing multiple resolutions and improving efficiency of similarity search. In addition, our data sources are based on participatory-sensing instead of using vehicle GPS systems. As more data be collected through participatory-sensing, more accurate crowdedness for a bus can be estimated. The experiment results further demonstrate the efficiency of our analytical approaches.

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2090515?needAccess=true

OCP: an OLAP-based bus crowdedness smart-perceiving mechanism for urban transportation

Designing deep-blue emitting materials with integrated high quantum yield and easy film-forming ability is challenging. As a typical organosilicon polymer, polycarbosilane (PCS) with −Si−C− repeated backbone has received much attention...

Shiwen Gong

Papers

OCP: an OLAP-based bus crowdedness smart-perceiving mechanism for urban transportation

Realizing highly efficient blue photoluminescence of dimethylsilane-aryl (phenylene, diphenylene, fluorenyl) main-chain polymers with σ-π conjugation