Phosphanyl Cyanophosphide Salts: Versatile PCN Building Blocks

The facile preparation of alkali salts of phosphanyl cyanophosphides [NHP‐PCN]^? (NHP=N‐heterocyclic phosphenium) is reported. Their formation is achieved by isoelectronic replacement of O for [N]^? in the phosphaketenes NHP‐PCO using alkaline hexamethyldisilazide M[N(SiMe₃)₂] (M=Na, K) as reagent. The new anionic entities are versatile PCN building blocks which allow the formation of a diversity of new cyanophosphine derivates including the first example of a PCNB hetero‐cumulene and a PCN‐ligated transition metal complex.     

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.     

AIM‐1: An Antibiotic‐Degrading Metallohydrolase That Displays Mechanistic Flexibility

Antibiotic resistance has emerged as a major threat to global health care. This is largely due to the fact that many pathogens have developed strategies to acquire resistance to antibiotics. Metallo‐β‐lactamases (MBL) have evolved to inactivate most of the commonly used β‐lactam antibiotics. AIM‐1 is one of only a few MBLs from the B3 subgroup that is encoded on a mobile genetic element in a major human pathogen. Here, its mechanism of action was characterised with a combination of spectroscopic and kinetic techniques and compared to that of other MBLs. Unlike other MBLs it appears that AIM‐1 has two avenues available for the turnover of the substrate nitrocefin, distinguished by the identity of the rate‐limiting step. This observation may be relevant with respect to inhibitor design for this group of enzymes as it demonstrates that at least some MBLs are very flexible in terms of interactions with substrates and possibly inhibitors.