Whilst graphene materials have become increasingly popular in recent years, the followed synthesis strategies face sustainability, environmental and quality challenges. This study proposes an effective, sustainable and scalable ultrasound-assisted mechano-chemical cracking method to produce graphene oxide (GO). A typical energy crop, miscanthus, was used as a carbon precursor and pyrolysed at 1200 °C before subjecting to edge-carboxylation via ball-milling in a CO2-induced environment. The resultant functionalised biochar was ultrasonically exfoliated in N-Methyl-2-pyrrolidone (NMP) and water to form GOs. The intermediate and end-products were characterised via X-ray diffraction (XRD), Raman, high-resolution transmission electron microscopy (HR-TEM) and atomic force microscopy (AFM) analyses. Results show that the proposed synthesis route can produce good quality and uniform GOs (8-10% monolayer), with up to 96% of GOs having three layers or lesser when NMP is used. Ultrasonication proved to be effective in propagating the self-repulsion of negatively-charged functional groups. Moreover, small amounts of graphene quantum dots were observed, illustrating the potential of producing various graphene materials via a single-step method. Whilst this study has only investigated utilising miscanthus, the current findings are promising and could expand the potential of producing good quality graphene materials from renewable sources via green synthesis routes.

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) outbreak has engulfed an unprepared world amidst a festive season. The zoonotic SARS-CoV-2, believed to have originated from infected bats, is the seventh member of enveloped RNA coronavirus. Specifically, the overall genome sequence of the SARS-CoV-2 is 96.2% identical to that of bat coronavirus termed BatCoV RaTG13. Although the current mortality rate of 2% is significantly lower than that of SARS (9.6%) and Middle East respiratory syndrome (MERS) (35%), SARS-CoV-2 is highly contagious and transmissible from human to human with an incubation period of up to 24 days. Some statistical studies have shown that, on average, one infected patient may lead to a subsequent 5.7 confirmed cases. Since the first reported case of coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 on December 1, 2019, in Wuhan, China, there has been a total of 60,412 confirmed cases with 1370 fatalities reported in 25 different countries as of February 13, 2020. The outbreak has led to severe impacts on social health and the economy at various levels. This paper is a review of the significant, continuous global effort that was made to respond to the outbreak in the first 75 days. Although no vaccines have been discovered yet, a series of containment measures have been implemented by various governments, especially in China, in the effort to prevent further outbreak, whilst various medical treatment approaches have been used to successfully treat infected patients. On the basis of current studies, it would appear that the combined antiviral treatment has shown the highest success rate. This review aims to critically summarize the most recent advances in understanding the coronavirus, as well as the strategies in prevention and treatment.

Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond.