Development of earth-abundant materials and low-cost processes for solar cells

textThe goal of renewable solar energy research is to develop low-cost, high-efficiency photovoltaic technologies. However, with the growing deployment of solar cells, approaching the terawatt scale, absorber materials reliant upon rare or unfriendly elements become a crucial issue. Thus, the primary objective of this dissertation is the development of a low-cost fabrication method for (i) thin-film solar cells and (ii) dye-sensitized solar cells using earth-abundant materials. In thin-film solar cells, the kesterite Cu₂ZnSnS₄ with earth abundant elements is used as an absorber layer. It possesses a high absorption coefficient, direct band gap, and good long-term stability compared to the traditional CdTe and Cu(In,Ga)(S,Se)₂ (CIGS) absorber layers. A facile hot-injection approach for synthesizing Cu₂ZnSn(S,Se)₄ nanocrystals with varied Se to (S+Se) ratio is developed to systematically investigate the role of Se in Cu₂ZnSn(S,Se)₄ nanocrystals and the evolution of Cu₂ZnSn(S,Se)₄ nanocrystals to Cu₂ZnSn(S,Se)₄ film during the sulfurization step to address the problems associated with its narrow compositional window and the loss of Sn during heat treatment. Additionally, the existing substrate-type device configuration for these solar cells uses a molybdenum (Mo) back contact, which suffers from serious disadvantages like the (i) presence of a Schottky barrier at the Mo/Cu₂ZnSn(S,Se)₄ interface and (ii) decomposition of Cu₂ZnSn(S,Se)₄ at the Mo interface. Accordingly, a low-cost and Mo-free superstrate-type device configuration of Au/Cu₂ZnSn(S,Se)₄/CdS/TiO₂/ITO/glass is developed to evaluate the conversion efficiency and to avoid the occurrence of a Schottky barrier at the interface and potential decomposition pathways induced by the formation of Mo(S,Se)₂. Furthermore, with the addition of ethyl cellulose, the loss of Sn associated with the conversion of CZTSe to CZTSSe during the grain growth process is mitigated, leading to an increase in the conversion efficiency compared to that of the precursor film without using ethyl cellulose. Such an improvement can provide insight into the grain growth of CZTSSe during the sulfurization process and thereby enhance the feasibility of sustainable, high efficiency CZTSSe solar devices. The excellent characteristics of dye-sensitized solar cells (DSSCs) with short energy-payback time, simple assembly, and eco-friendly features make them a potential option to utilize solar energy. Accordingly, a facile, low-cost, template-free route for TiO₂ hollow submicrospheres embedded with SnO₂ nanobeans is developed for use as a versatile scattering layer in DSSCs. Our designed structure simultaneously promotes dye adsorption, light harvesting, and electron transport, leading to a 28 % improvement in the conversion efficiency as compared with the film-based SnO₂. In addition, a naturally-derived carbonaceous material as a Pt-free counter electrode for DSSCs is also developed for the first time: carbonized sucrose-coated eggshell membrane (CSEM). It is found that the carbonized sucrose-coated eggshell membranes consist of unique micropores of less than 2 nm, which effectively catalyze the triiodide into iodide in the light-electricity conversion process, leading to an improvement in the V [subscript oc] and a competitive efficiency as compared to that of a DSSC with a traditional Pt-based counter electrode.Materials Science and Engineerin

SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training

The pre-training for language models captures general language understanding but fails to distinguish the affective impact of a particular context to a specific word. Recent works have sought to introduce contrastive learning (CL) for sentiment-aware pre-training in acquiring affective information. Nevertheless, these methods present two significant limitations. First, the compatibility of the GPU memory often limits the number of negative samples, hindering the opportunities to learn good representations. In addition, using only a few sentiment polarities as hard labels, e.g., positive, neutral, and negative, to supervise CL will force all representations to converge to a few points, leading to the issue of latent space collapse. This study proposes a soft momentum contrastive learning (SoftMCL) for fine-grained sentiment-aware pre-training. Instead of hard labels, we introduce valence ratings as soft-label supervision for CL to fine-grained measure the sentiment similarities between samples. The proposed SoftMCL is conducted on both the word- and sentence-level to enhance the model's ability to learn affective information. A momentum queue was introduced to expand the contrastive samples, allowing storing and involving more negatives to overcome the limitations of hardware platforms. Extensive experiments were conducted on four different sentiment-related tasks, which demonstrates the effectiveness of the proposed SoftMCL method. The code and data of the proposed SoftMCL is available at: https://www.github.com/wangjin0818/SoftMCL/.Comment: Accepted by LREC-COLING 202

The impact of information and communication technology (ICT) on the dynamic capabilities of supply chains

This study aimed at empirically examining the impact of information and communication technology interaction intensity among supply chain members on the dynamic capabilities of organizations. The study took Taiwan's top 1000 manufacturers as the study population, whereas the relationship between the manufacturers and their suppliers was taken as the research unit, and the respondents consisted of the executives or senior procurement specialists dealing with suppliers in the organizations. LISREL was used to verify models and test their goodness-of-fit. In the data analysis, the parameters were estimated with the default maximum likelihood estimation method. Empirical results of this research can help businesses take a closer look into how the intensity of supply chain ICT interaction impacts the dynamic capabilities of the supply chain members, so that businesses can hold on to different intensities of their supply chain ICT interactions to increase the relationship commitment and trust among the supply chain members, and further promote their dynamic capabilities

Personalized LoRA for Human-Centered Text Understanding

Effectively and efficiently adapting a pre-trained language model (PLM) for human-centered text understanding (HCTU) is challenging since user tokens are million-level in most personalized applications and do not have concrete explicit semantics. A standard and parameter-efficient approach (e.g., LoRA) necessitates memorizing numerous suits of adapters for each user. In this work, we introduce a personalized LoRA (PLoRA) with a plug-and-play (PnP) framework for the HCTU task. PLoRA is effective, parameter-efficient, and dynamically deploying in PLMs. Moreover, a personalized dropout and a mutual information maximizing strategies are adopted and hence the proposed PLoRA can be well adapted to few/zero-shot learning scenarios for the cold-start issue. Experiments conducted on four benchmark datasets show that the proposed method outperforms existing methods in full/few/zero-shot learning scenarios for the HCTU task, even though it has fewer trainable parameters. For reproducibility, the code for this paper is available at: https://github.com/yoyo-yun/PLoRA.Comment: Accepted by AAAI 202

Continuous epidermal growth factor receptor-tyrosine kinase inhibitor administration in primary lung adenocarcinoma patients harboring favorable mutations with controlled target lung tumors dose not hinder survival benefit despite small new lesions

AbstractBackgroundIn this study, we investigated the efficacy of continuous epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) administration in lung adenocarcinoma patients harboring favorable mutations regarding the progressive disease (PD) status with appearance of indolent new lesions.MethodsFrom June 2010 to October 2012, 102 patients with lung adenocarcinoma, harboring favorable EGFR mutations and treated with EGFR-TKI were analyzed. Definite new lesions were detected during EGFR-TKI therapy, even though the primary target tumors were controlled.ResultsOf the 102 patients, 57 continued and 45 discontinued EGFR-TKI therapy. The median overall survival was 529 days for the discontinuation group and 791 days for the continuation group (p = 0.0197). Median survival time after the discontinuation of EGFR-TKI was 181 days and 115 days in the discontinuation and continuation groups, respectively (p = 0.1776), whereas median survival time after the appearance of indolent new lesions was 204 days and 262 days, respectively (p = 0.0237).ConclusionContinuous EGFR-TKI administration in favorable EGFR-mutative lung adenocarcinoma patients with controlled primary tumors did not hinder the survival benefit, despite the appearance of new lesions

Probing the A1 to L10 Transformation in FeCuPt Using the First Order Reversal Curve Method

The A1- L10 phase transformation has been investigated in (001) FeCuPt thin films prepared by atomic-scale multilayer sputtering and rapid thermal annealing (RTA). Traditional x-ray diffraction is not always applicable in generating a true order parameter, due to non-ideal crystallinity of the A1 phase. Using the first-order reversal curve (FORC) method, the A1 and L10 phases are deconvoluted into two distinct features in the FORC distribution, whose relative intensities change with the RTA temperature. The L10 ordering takes place via a nucleation-and-growth mode. A magnetization-based phase fraction is extracted, providing a quantitative measure of the L10 phase homogeneity.Comment: 17 pages, 5 figures, 4 page supplementary material (4 figures