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This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

This paper aims to improve the permeability and antifouling of polysulfone (PSF) ultrafiltration membranes, the PSF matrix was modified by incorporating sulfonated polysulfone (SPSF).

Systematic investigations were conducted on the synergistic effects of a pore-forming agent, coagulation bath temperature and SPSF doping in the casting solution on blended ultrafiltration membranes. The chemical composition of the membranes was analyzed using Fourier transform infrared spectroscopy. The morphology and surface roughness of the membranes were characterized using scanning electron microscopy and atomic force microscopy. The hydrophilicity of the membrane surface was analyzed using a contact angle meter. The permeability and antifouling properties of the blended membranes were also investigated through filtration experiments.

The results indicated that the blended ultrafiltration membranes demonstrated an optimal overall performance when PVP-K30 content was 5.0 Wt.%, coagulation bath temperature was 30°C and SPSF content was 2.4 Wt.%. In comparison to a pure PSF ultrafiltration membrane, there was a significant increase in pure water flux (390.7 L·m⁻²·h⁻¹) by 2.2 times, while bovine serum albumin retention slightly decreased to 93.8%. In addition, the flux recovery rate improved by 2.1 times (71.4%) compared to that of the original PSF ultrafiltration membrane.

The method provided a simple and practical solution for improving the antifouling and permeability of PSF ultrafiltration membranes.

SPSF was anticipated to serve as an excellent modification additive for the preparation of ultrafiltration membranes with superior properties.