Enhancing Conductivity in Silicon Heterojunction Solar Cells with Silver Nanowire-Assisted Ti3C2Tx MXene Electrodes for Cost-Effective and Scalable Photovoltaics.

Silicon heterojunction (SHJ) solar cells have set world-record efficiencies among single-junction silicon solar cells, accelerating their commercial deployment. Despite these clear efficiency advantages, the high costs associated with low-temperature silver pastes (LTSP) for metallization have driven the search for more economical alternatives in mass production. 2D transition metal carbides (MXenes) have attracted significant attention due to their tunable optoelectronic properties and metal-like conductivity, the highest among all solution-processed 2D materials. MXenes have emerged as a cost-effective alternative for rear-side electrodes in SHJ solar cells. However, the use of MXene electrodes has so far been limited to lab-scale SHJ solar cells. The efficiency of these devices has been constrained by a fill factor (FF) of under 73%, primarily due to suboptimal charge transport at the contact layer/MXene interface. Herein, a silver nanowire (AgNW)-assisted Ti3C2Tx MXene electrode contact is introduced and explores the potential of this hybrid electrode in industry-scale solar cells. By incorporating this hybrid electrode into SHJ solar cells, 9.0 cm2 cells are achieved with an efficiency of 24.04% (FF of 81.64%) and 252 cm2 cells with an efficiency of 22.17% (FF of 76.86%), among the top-performing SHJ devices with non-metallic electrodes to date. Additionally, the stability and cost-effectiveness of these solar cells are discussed.

Clinical manifestation score and characterization of cytokines and lymphocytes of dimethylacetamide-induced toxic hepatitis in spandex workers.

Occupational exposure to dimethylacetamide (DMAc) has been reported to cause toxic hepatitis. Sixty spandex workers were included in this study to research the clinical manifestations and expression of cytokines and lymphocytes in DMAc-induced toxic hepatitis. Chinese drugs (reduced glutathione and Hugan tablets) were used to treat them. The manifestations including jaundice, asthenia, appetite, nausea, emesis, abdominal distension, yellow urine, and dizziness were scored. The percentages of patients rated as 0-3, 4-6, 7-9, and 10-12 points were 33.3%, 43.3%, 21.7%, and 1.7%, respectively, before treatment, and all patients showed 0-3 points after the treatment. The ultrasonic and CT imaging revealed diffuse intrahepatic hypodensity, intrahepatic calcification, signs of liver injury, and splenomegaly, which improved after therapy. Blood analysis showed that ALT, AST, TBIL, IL-6, IL-10, TNF-α, IFN-γ, CD3+%, and CD4+/CD8+ statistically decreased after drug treatment. Correlation analysis demonstrated positive linear correlations between ALT and TBIL, AST and TBIL, IL-10 and ATL, IL-10 and AST, IL-10 and TBIL, IFN-γ and IL-6, IFN-γ and TNF-α, and CD3+% and ALT. Pro-inflammatory cytokines and lymphocytes in DMAc-induced toxic hepatitis reflected an active immune state that decreased after treatment. IL-10 may inhibit the immune response in this disease, as a protective mechanism.

A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining.

Mining frequent subgraphs from a collection of input graphs is an important task for exploratory data analysis on graph data. However, if the input graphs contain sensitive information, releasing discovered frequent subgraphs may pose considerable threats to individual privacy. In this paper, we study the problem of frequent subgraph mining (FSM) under the rigorous differential privacy model. We present a two-phase differentially private FSM algorithm, which is referred to as DFG. In DFG, frequent subgraphs are privately identified in the first phase, and the noisy support of each identified frequent subgraph is calculated in the second phase. In particular, to privately identity frequent subgraphs, we propose a frequent subgraph identification approach, which can improve the accuracy of discovered frequent subgraphs through candidate pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support computation approach, which leverages the inclusion relations between the discovered frequent subgraphs to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that DFG satisfies ϵ-differential privacy. Extensive experimental results on real datasets show that DFG can privately find frequent subgraphs while achieving high data utility.

Differentially Private Frequent Sequence Mining.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively prune those unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose PFS2, a novel differentially private FSM algorithm. It is the first algorithm that supports the general gap-constrained FSM in the context of differential privacy. The gap constraints in FSM can be used to limit the mining results to a controlled set of frequent sequences. In our PFS2 algorithm, the core is to utilize sample databases to prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which candidate sequences are potentially frequent. To improve the accuracy of such private estimations, a gap-aware sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to calibrate the amount of noise required by differential privacy, a gap-aware sensitivity computation method is proposed to obtain the sensitivity of the local support computations with different gap constraints. Furthermore, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ϵ-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy.

Improved amorphous/crystalline silicon interface passivation for heterojunction solar cells by low-temperature chemical vapor deposition and post-annealing treatment.

In this study, hydrogenated amorphous silicon (a-Si:H) thin films are deposited using a radio-frequency plasma-enhanced chemical vapor deposition (RF-PECVD) system. The Si-H configuration of the a-Si:H/c-Si interface is regulated by optimizing the deposition temperature and post-annealing duration to improve the minority carrier lifetime (τeff) of a commercial Czochralski (Cz) silicon wafer. The mechanism of this improvement involves saturation of the microstructural defects with hydrogen evolved within the a-Si:H films due to the transformation from SiH2 into SiH during the annealing process. The post-annealing temperature is controlled to ∼180 °C so that silicon heterojunction solar cells (SHJ) could be prepared without an additional annealing step. To achieve better performance of the SHJ solar cells, we also optimize the thickness of the a-Si:H passivation layer. Finally, complete SHJ solar cells are fabricated using different temperatures for the a-Si:H film deposition to study the influence of the deposition temperature on the solar cell parameters. For the optimized a-Si:H deposition conditions, an efficiency of 18.41% is achieved on a textured Cz silicon wafer.

Inorganic Perovskite Surface Reconfiguration for Stable Inverted Solar Cells with 20.38% Efficiency and Its Application in Tandem Devices.

Inorganic perovskite solar cells (IPSCs) have garnered attention in tandem solar cells (TSCs) due to their suitable bandgap and impressive thermal stability. However, the efficiency of inverted IPSCs has been limited by the high trap density on the top surface of inorganic perovskite film. Herein, a method for fabricating efficient IPSCs by reconfiguring the surface properties of CsPbI2.85 Br0.15 film with 2-amino-5-bromobenzamide (ABA) is developed. This modification not only exhibits the synergistic coordination of carbonyl (C=O) and amino (NH2 ) groups with uncoordinated Pb2+ , but also the Br fills halide vacancies and suppresses the formation of Pb0 , effectively passivating the defective top surface. As a result, a champion efficiency of 20.38%, the highest efficiency reported for inverted IPSCs to date is achieved. Furthermore, the successful fabrication of a p-i-n type monolithic inorganic perovskite/silicon TSCs with an efficiency of 25.31% for the first time is demonstrated. Crucially, the unencapsulated ABA-treated IPSCs shows enhanced photostability, retaining 80.33% of its initial efficiency after 270 h, and thermal stability (maintain 85.98% of its initial efficiency after 300 h at 65 °C). The unencapsulated ABA-treated TSCs also retains 92.59% of its initial efficiency after 200 h under continuous illumination in ambient air.

State functions/quantities in thermodynamics and heat transfer.

In thermodynamics, it is essential to distinguish between state functions and process functions. The reason is that the simple compressible thermodynamic system is a bivariate-process system, and the change of internal energy, a state function, corresponds to two process functions, heat and work. Among the state functions in thermodynamics, entropy is a special one because it has to be defined through a process function, exchanged heat δ Q , and a unique factor of integration, 1/T. In heat transfer, it is shown that Fourier's law and the differential equation of heat conduction are both relations of state quantities alone, and process quantities appear when an integration with respect to time is applied. Moreover, an incompressible heat conduction medium element without conversion between heat and work is a univariate-process system governed by a single variable, temperature. In this case, the change of the thermal energy ("heat content") stored in the system, a state quantity as a function of T alone, corresponds to only one process quantity, the transferred heat. Therefore, on the one hand, it is unnecessary to strictly distinguish between state quantities and process quantities in heat transfer, and on the other hand, there is no need to use a factor of integration to prove entransy a state quantity in heat transfer. Thermodynamics and heat transfer are two parallel sub-disciplines in thermal science. It is incorrect to deny entransy as a state quantity in heat transfer by the uniqueness of the factor of integration for entropy in thermodynamics, and entransy has significant physical meaning in the analysis and optimization of heat transfer processes.

Differentially Private Frequent Subgraph Mining.

Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.

Differentially Private Frequent Sequence Mining via Sampling-based Candidate Pruning.

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively reduce the number of unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose a novel differentially private FSM algorithm, which is referred to as PFS2. The core of our algorithm is to utilize sample databases to further prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which sequences are potentially frequent. To improve the accuracy of such private estimations, a sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy.