NanoRefinery of carbonaceous nanomaterials: Complementing dairy manure gasification and their applications in cellular imaging and heavy metal sensing

This article describes an efficient method, combining chemical oxidation and acetone extraction, to produce carbonaceous nanomaterials from dairy manure biochar. The optical and mechanical properties are similar to methods previously reported carbonaceous nanomaterials from biomass. Our novel process cuts the processing time in half and drastically reduces the energy input required. The acetone extraction produced 10 fractions with dairy manure biochar-derived carbonaceous nanomaterials (DMB-CNs). The fraction with the carbonaceous nanomaterials, DMB-CN-E1, with highest fluorescence was selected for in-depth characterisation and for initial testing across a range of applications. DMB-CN-E1 was characterised using atomic force microscope, electrophoresis, and spectrophotometric methods. DMB-CN-E1 exhibited a lateral dimension between 11 and 28 nm, a negative charge, and excitation/emission maxima at 337/410 nm, respectively. The bioimaging potential of DMB-CN-E1 evidenced different locations and different interactions with the cellular models evaluated. DMB-CN-E1 was quenched by several heavy metal ions showing a future application of these materials in heavy metal ion detection and/or removal. The demonstrated capabilities in bioimaging and environmental sensing create the opportunity for generating added-value nanomaterials (NanoRefinery) from dairy manure biochar gasification and, thus, increasing the economic viability of gasification plants.

Multivariate analysis of biochar-derived carbonaceous nanomaterials for detection of heavy metal ions in aqueous systems

This article focuses on implementing multivariate analysis to evaluate biochar-derived carbonaceous nanomaterials (BCN) from three different feedstocks for the detection and differentiation of heavy metal ions in aqueous systems. The BCN were produced from dairy manure, rice straw and sorghum straw biochar using our NanoRefinery process. The NanoRefinery process transforms biochar into advanced nanomaterials using depolymerisation/chemical oxidation and purification of nanomaterials using solvent extraction. Dairy manure biochar-derived carbonaceous nanomaterials (DMB-CN), rice straw biochar-derived carbonaceous nanomaterials (RSB-CN) and sorghum straw biochar-derived carbonaceous nanomaterials (SSB-CN) were utilised as probes for the evaluation of their fluorescent properties and the detection of heavy metal ions. The BCN fluorescence quenching and fluorescence recovery was tested with lead (Pb²⁺), nickel (Ni²⁺), copper (Cu²⁺) and mercury (Hg²⁺). Principal component analysis (PCA) and discriminant analysis were used to differentiate among heavy metal ions in water samples. The BCN from different feedstocks had different characteristics and produced different interactions with heavy metal ions. DMB-CN had the highest quenching for Hg²⁺ and Ni²⁺ while SSB-CN and RSB-CN responded best to Cu²⁺ and Pb²⁺, respectively. The fluorescence quenching was modelled using linear and empirical functions. PCA and discriminant analysis used the quenching measurements to differentiate heavy metal ions in aqueous system. A key result was that the discriminant analysis had a 100% accuracy to detect Pb²⁺, 66% for Ni²⁺ and Cu²⁺, and 33% for Hg²⁺. This study has shown that biochar-derived carbonaceous nanomaterials could be used in heavy metal ions sensing applications. This is the first step in the development of a fast and accurate method for the detection of heavy metal ions in waters using environmentally friendly BCN.

Microalgae biochar-derived carbon dots and their application in heavy metal sensing in aqueous systems

This research seeks a coupled solution for managing the large amounts of biochar produced by microalgae biofuel production, and the necessity for novel, economic and accurate heavy metal sensing methods. Therefore, this study evaluated the transformation of microalgae biochar (MAB) into carbon dots (Cdots) and their subsequent application as heavy metal ion sensors in aqueous systems. The experimental phase included the transformation of MAB into microalgae biochar-derived carbon dots (MAB-Cdots), MAB-Cdot characterisation and the evaluation of the MAB-Cdots as transducers for the detection of four heavy metal ions (Pb²⁺, Cu²⁺, Cd²⁺, and Ni²⁺). MAB-Cdot fluorescence was stable over a wide range of pH and resistant to photo-bleaching, making them suitable as fluorescence probes. The MAB-Cdot fluorescence was quenched by all of the metal ions and displayed different quenching levels. Depending upon the ions involved, MAB-Cdots were used to detect the presence of heavy metal ions from concentrations of 0.012 μM up to 2 mM by measuring the reduction in fluorescence intensity. Neutral and slightly alkaline pHs were optimal for Cu²⁺ Ni²⁺ and Pb²⁺ heavy metal quenching. To quantify the concentration of the heavy metal ions, linear and logarithmic functions were used to model the MAB-Cdot fluorescence quenching. The sensing mechanism was determined to be reversible and purely collisional with some fluorophores less accessible than the others. This work demonstrated the ability to produce Cdots from microalgae biochar, examined their application as a transducer for detecting heavy metal ions in aqueous systems and paves the way for novel sensing systems using MAB-Cdots.

Complexity, fractals, disease time, and cancer

Despite many years of research, a method to precisely and quantitatively determine cancer disease state remains elusive. Current practice for characterizing solid tumors involves the use of varying systems of tumor grading and staging and thus leaves diagnosis and clinical staging dependent on the experience and skill of the physicians involved. Although numerous disease markers have been identified, no combination of them has yet been found that produces a quantifiable and reliable measure of disease state. Newly developed genomic markers and other measures based on the developing sciences of complexity offer promise that this situation may soon be changed for the better. In this paper, we examine the potential of two measures of complexity, fractal dimension and percolation, for use as components of a yet to be determined "disease time" vector that more accurately quantifies disease state. The measures are applied to a set of micrographs of progressive rat hepatoma and analyzed in terms of their correlation with cell differentiation, ratio of tumor weight to rat body weight and tumor growth time. The results provide some support for the idea that measures of complexity could be important elements of any future cancer "disease time" vector.