From full life cycle assessment to simplified life cycle assessment: A generic methodology applied to sludge treatment

Life cycle assessment is widely used to evaluate sludge treatment methods. However, detailed life cycle assessment is time-consuming and resource-intensive. In addition, the complex characteristics of sludge and the variety of treatment methods increase the difficulty of sludge treatment life cycle assessment. There is an urgent need to develop simplified life cycle assessment models to allow rapid decision-making. This study proposes a simplified method of sludge treatment life cycle assessment based on logistic regression analysis. The simplified model relies on the total environmental impact and very few or even a single input parameters. Contribution and data quality analysis were introduced to identify key input parameters and to obtain model training data. The method was eventually applied to traditional sludge treatment methods, showing that it can quickly and accurately predict environmental impact. Representative analysis showed that the model would be affected by technology and energy structure modification. It is hoped that this study will provide a reference and new perspectives for a simplified life cycle assessment of sludge treatment.

Use of simplified models for theoretical prediction of the interactions between available antibodies and the receptor-binding domain of SARS-CoV-2 spike protein

The negative impact of infectious diseases like COVID-19 on public health and the global economy is evident. This pandemic represents a significant challenge for the scientific community to develop new practical analytical methods for accurately diagnosing emerging cases. Due to their selectivity and sensitivity, new methodologies based on antigen/antibody interactions to detect COVID-19 biomarkers are necessary. In this context, the theoretical, computational modeling reduces experimental efforts and saves resources for rational biosensor design. This study proposes using molecular dynamics to predict the interactions between the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein simplified model and a set of highly characterized antibodies. The binding free energy of the antigen/antibody complexes was calculated for the simplified models and compared against the complete SARS-CoV-2 ectodomain to validate the methodology. The structural data derived from our molecular dynamics and end-point free energy calculations showed a positive correlation between both approximations, with a 0.82 Pearson correlation coefficient; t = 3.661, df = 3, p-value = 0.03522, with a 95% confident interval. Furthermore, we identified the interfacial residues that could generate covalent bonds with a specific chemical surface without perturbing the binding dynamics to develop highly sensitive and specific diagnostic devices. Communicated by Ramaswamy H. Sarma.