Anthropogenic radionuclides contaminate a range of environments as a result of nuclear activities, for example, leakage from waste storage tanks/ponds (e.g. Hanford, USA or Sellafield sites, UK) or as a result of large scale nuclear accidents (e.g. Chernobyl, Ukraine or Fukushima, Japan). One of the most widely applied remediation techniques for contaminated waters is the use of sorbent materials (e.g. zeolites and apatites). However, a key problem at nuclear contaminated sites is the remediation of radionuclides from complex chemical environments. In this study, biogenic hydroxyapatite (BHAP) produced by Serratia sp. bacteria was investigated for its potential to remediate surrogate radionuclides (Sr2+ and Co2+) from environmentally relevant waters by varying pH, salinity and the type and concentration of cations present. The sorption capacity of the BHAP for both Sr2+ and Co2+ was higher than for a synthetically produced hydroxyapatite (HAP) in the solutions tested. BHAP also compared favorably against a natural zeolite (as used in industrial decontamination) for Sr2+ and Co2+ uptake from saline waters. Results confirm that hydroxyapatite minerals of high surface area and amorphous calcium phosphate content, typical for biogenic sources, are suitable restoration or reactive barrier materials for the remediation of complex contaminated environments or wastewaters.

Fine-tuning a large language model on downstream tasks has become a commonly adopted process in the Natural Language Processing (NLP) . However, such a process, when associated with the current transformer-based (Vaswani et al., 2017) architectures, shows several limitations when the target task requires to reason with long documents. In this work, we introduce a novel hierarchical propagation layer that spreads information between multiple transformer windows. We adopt a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers. We validate the effectiveness of our approach on three extractive summarization corpora of long scientific papers and news articles. We compare our approach to standard and pre-trained language-model-based summarizers and report state-of-the-art results for long document summarization and comparable results for smaller document summarization.