Mining minerals and critical raw materials from bittern: Understanding metal ions fate in saltwork ponds.

Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and Ge) are also contained in this "liquid mine". Most of them are considered as Critical Raw Materials by the European Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation saltwork ponds. However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic strengths expected (0.5-7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution can be adopted to provide valuable description of the systems. In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity in halite and the major impurities contained, mainly Ca, Mg and sulphate species; ii) the fate of minor components as B, Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials mining from seawater brine, for process intensification and combination with desalination.

[Can Big Data change our practices?] / Le Big Data peut-il changer nos pratiques ?

The European Medicines Agency has defined Big Data by the "3 V's": Volume, Velocity and Variety. These large databases allow access to real life data on patient care. They are particularly suited for studies of adverse events and pharmacoepidemiology. Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data using model architectures, which are composed of multiple nonlinear transformations. This article shows how Big Data and Deep Learning can help in ophthalmology, pointing out their advantages and disadvantages. A literature review is presented in this article illustrating the uses of Deep Learning in ophthalmology.

Experimental investigation and modeling of diffusion dialysis for HCl recovery from waste pickling solution.

Hydrochloric acid recovery from pickling solutions was studied by employing a batch diffusion dialysis (DD) laboratory test-rig equipped with Fumasep membranes. The effect of main operating parameters such as HCl concentration (0.1-3 M) and the presence of Fe2+ (up to 150 g/l) was investigated to simulate the system operation with real industrial streams. The variation of HCl, Fe2+ and water flux was identified. When only HCl is present, a recovery efficiency of 100% was reached. In the presence of FeCl2, higher acid recovery efficiencies, up to 150%, were observed due to the so-called "salt effect", which promotes the passage of acid even against its concentration gradient. A 7% leakage of FeCl2 was detected in the most severe conditions. An original analysis on water flux in DD operation has indicated that osmotic flux prevails at low HCl concentrations, while a dominant "drag flux" in the opposite direction is observed for higher HCl concentrations. A comprehensive mathematical model was developed and validated with experimental data. The model has a time and space distributed-parameters structure allowing to effectively simulate steady-state and transient batch operations, thus providing an operative tool for the design and optimisation of DD units.

Implant site preparation using ultrasonic technique vs. Conventional drilling: three-year follow-up of a split-mouth study.

The aim of this split-mouth study was to evaluate the effectiveness of ultrasonic technique for implant site preparation when compared with the conventional drilling technique. A total of 98 bilateral single implants were placed in 49 patients. For each patient, traditional drilling technique was used at one side (control) and ultrasonic technique was used at the other side (test). Prior to insertion all implants were carefully embedded in liquid plasma rich in growth factors. Clinical and radiographic results were assessed after three years of function. The same implant success rate (95.9%) was recorded for both groups in a three year follow up, with only two implants lost per group. At the last follow-up the mean peri-implant bone loss was 0.42 ± 0.14 mm and 0.33 ± 0.13 mm in the control and test group, respectively. The difference was significant (P<0.001). In conclusion, ultrasonic technique for implant site preparation showed similar clinical performance but better preservation of the peri-implant bone. Together with the known characteristics of safety and reduced bleeding at the surgical site the ultrasonic technique represents a valuable alternative to traditional drilling.