Mimicking Biosintering: The Identification of Highly Condensed Surfaces in Bioinspired Silica Materials.

Interfacial interactions between inorganic surfaces and organic additives are vital to develop new complex nanomaterials. Learning from biosilica materials, composite nanostructures have been developed, which exploit the strength and directionality of specific polyamine additive-silica surface interactions. Previous interpretations of these interactions are almost universally based on interfacial charge matching and/or hydrogen bonding. In this study, we analyzed the surface chemistry of bioinspired silica (BIS) materials using solid-state nuclear magnetic resonance (NMR) spectroscopy as a function of the organic additive concentration. We found significant additional association between the additives and fully condensed (Q4) silicon species compared to industrial silica materials, leading to more overall Q4 concentration and higher hydrothermal stability, despite BIS having a shorter synthesis time. We posit that the polyfunctionality and catalytic activity of additives in the BIS synthesis lead to both of these surface phenomena, contrasting previous studies on monofunctional surfactants used in most other artificial templated silica syntheses. From this, we propose that additive polyfunctionality can be used to generate tailored artificial surfaces in situ and provide insights into the process of biosintering in biosilica systems, highlighting the need for more in-depth simulations on interfacial interactions at silica surfaces.

Degradation of Anthraquinone Dyes from Effluents: A Review Focusing on Enzymatic Dye Degradation with Industrial Potential.

Up to 84 000 tons of dye can be lost in water, and 90 million tons of water are attributed annually to dye production and their application, mainly in the textile and leather industry, making the dyestuff industry responsible for up to 20% of the industrial water pollution. The majority of dyes industrially used today are aromatic compounds with complex, reinforced structures, with anthraquinone dyes being the second largest produced in terms of volume. Despite the progress on decolorization and degradation of azo dyes, very little attention has been given to anthraquinone dyes. Anthraquinone dyes pose a serious environmental problem as their reinforced structure makes them difficult to degrade naturally. Existing methods of decolorization might be effective but are neither efficient nor practical due to extended time, space, and cost requirements. Attention should be given to the emerging routes for dye decolorization via the enzymatic action of oxidoreductases, which have already a strong presence in various other bioremediation applications. This review will discusses the presence of anthraquinone dyes in the effluents and ways for their remediation from dyehouse effluents, focusing on enzymatic processes.

Survival probability of a lazy prey on lattices and complex networks.

We develop a framework to analyse the survival probability of a prey following a minimal effort evasion strategy, that is being chased by N predators on finite lattices or complex networks. The predators independently perform random walks if the prey is not within their sighting radius, whereas, the prey only moves when a predator moves onto a node within its sighting radius. We verify the proposed framework on three different finite lattices with periodic boundaries through numerical simulations and find that the survival probability (Psur) decays exponentially with a decay rate proportional to P(N, k) (number of permutations), where k is the minimum number of predators required to capture a prey. We then extend the framework onto complex networks and verify its robustness on the networks generated by the Watts-Strogatz (W-S), Barabási-Albert (B-A) models and a few real-world networks. Our analysis predicts that, for the considered lattices, Psur reduces as the degree of the nodes of the lattice is increased. However, for networks, Psur initially increases with the average degree of the nodes, reaches a maximum, and then decreases. Furthermore, we analyse the effect of the long-range connections in networks on Psur in W-S networks. The proposed framework enables one to study the survival probability of such a prey being hunted by multiple predators on any given structure.

Reconstruction of multiplex networks via graph embeddings.

Multiplex networks are collections of networks with identical nodes but distinct layers of edges. They are genuine representations of a large variety of real systems whose elements interact in multiple fashions or flavors. However, multiplex networks are not always simple to observe in the real world; often, only partial information on the layer structure of the networks is available, whereas the remaining information is in the form of aggregated, single-layer networks. Recent works have proposed solutions to the problem of reconstructing the hidden multiplexity of single-layer networks using tools proper for network science. Here, we develop a machine-learning framework that takes advantage of graph embeddings, i.e., representations of networks in geometric space. We validate the framework in systematic experiments aimed at the reconstruction of synthetic and real-world multiplex networks, providing evidence that our proposed framework not only accomplishes its intended task, but often outperforms existing reconstruction techniques.

Symmetry breaking in optimal transport networks.

Engineering multilayer networks that efficiently connect sets of points in space is a crucial task in all practical applications that concern the transport of people or the delivery of goods. Unfortunately, our current theoretical understanding of the shape of such optimal transport networks is quite limited. Not much is known about how the topology of the optimal network changes as a function of its size, the relative efficiency of its layers, and the cost of switching between layers. Here, we show that optimal networks undergo sharp transitions from symmetric to asymmetric shapes, indicating that it is sometimes better to avoid serving a whole area to save on switching costs. Also, we analyze the real transportation networks of the cities of Atlanta, Boston, and Toronto using our theoretical framework and find that they are farther away from their optimal shapes as traffic congestion increases.

Feasibility and Advantages of Continuous Synthesis of Bioinspired Silica Using CO2 as an Acidifying Agent.

In this work, we present a method for the continuous synthesis of bioinspired porous silica (BIS) particles using carbon dioxide (CO2) as an acidifying agent. Typical BIS synthesis uses strong mineral acids (e.g., HCl) to initiate the hydrolysis and subsequent condensation reactions. The use of strong acids leads to challenges in controlling the reaction pH. The synthesis approach proposed in this work offers for the first time CO2 as an attractive alternative for the synthesis of BIS and demonstrates the continuous process. The developed method leverages the mild acidic and the self-buffering nature of the CO2 combined with additional options for controlling mass transfer rates to facilitate enhanced control of pH, which is crucial for controlling the properties of synthesized BIS. Proof of concept experiments conducted in continuous mode demonstrated a yield of over 70% and a surface area exceeding 500 m2/g. These results indicate the successful synthesis of BIS using CO2 with properties in the desired range. The enhanced pH control offered by this CO2-based process will facilitate the implementation of a sustainable and robust continuous process for BIS synthesis.

Quality-by-Design Approach to Process Intensification of Bioinspired Silica Synthesis.

Characterizing nanomaterials is challenging due to their macromolecular nature, requiring suites of physicochemical analysis to fully resolve their structure. As such, their synthesis and scale-up are notoriously complex, especially when compared to small molecules or bulk crystalline materials, which can be provided a unique fingerprint from nuclear magnetic resonance (NMR) or X-ray diffraction (XRD) alone. In this study, we address this challenge by adopting a three-step quality-by-design (QbD) approach to the scale-up of bioinspired silica nanomaterials, demonstrating its utility toward synthesis scale-up and intensification for this class of materials in general. First, we identified material-specific surface area, pore-size distribution, and reaction yield as critical quality attributes (CQAs) that could be precisely measured and controlled by changing reaction conditions. We then identified the critical process parameters (CPPs) controlling bioinspired synthesis properties, exploring different process routes, incorporating commercial reagents, and optimizing reagent ratios, comparing silica properties against original CQA values to identify acceptable limits to each CPP. Finally, we intensified the synthesis by increasing reagent concentration while simultaneously incorporating the optimized CPPs, thereby modifying the bioinspired silica synthesis to make it compatible with existing manufacturing methods. We increased the specific yield from ca. 1.1 to 38 g/L and reduced the additive intensity from ca. 1 to 0.04 g/g product, greatly reducing both synthesis cost and waste production. These results identify a need for mapping the effects of critical process parameters on material formation pathways and CQAs to enable accelerated scale-up and transition from the lab to the market.

Influence maximization: Divide and conquer.

The problem of influence maximization, i.e., finding the set of nodes having maximal influence on a network, is of great importance for several applications. In the past two decades, many heuristic metrics to spot influencers have been proposed. Here, we introduce a framework to boost the performance of such metrics. The framework consists in dividing the network into sectors of influence, and then selecting the most influential nodes within these sectors. We explore three different methodologies to find sectors in a network: graph partitioning, graph hyperbolic embedding, and community structure. The framework is validated with a systematic analysis of real and synthetic networks. We show that the gain in performance generated by dividing a network into sectors before selecting the influential spreaders increases as the modularity and heterogeneity of the network increase. Also, we show that the division of the network into sectors can be efficiently performed in a time that scales linearly with the network size, thus making the framework applicable to large-scale influence maximization problems.

Multiplex reconstruction with partial information.

A multiplex is a collection of network layers, each representing a specific type of edges. This appears to be a genuine representation of many real-world systems. However, due to a variety of potential factors, such as limited budget and equipment, or physical impossibility, multiplex data can be difficult to observe directly. Often, only partial information on the layer structure of the system is available, whereas the remaining information is in the form of a single-layer network. In this work we face the problem of reconstructing the hidden multiplex structure of an aggregated network from partial information. We propose an algorithm that leverages the layerwise community structure that can be learned from partial observations to reconstruct the ground-truth topology of the unobserved part of the multiplex. The algorithm is characterized by a computational time that grows linearly with the network size. We perform a systematic study of reconstruction problems for both synthetic and real-world multiplex networks. We show that the ability of the proposed method to solve the reconstruction problem is affected by the heterogeneity of the individual layers and the similarity among the layers. On real-world networks, we observe that the accuracy of the reconstruction saturates quickly as the amount of available information increases. In genetic interaction and scientific collaboration multiplexes, for example, we find that 10% of ground-truth information yields 70% accuracy, while 30% information allows for more than 90% accuracy.

A Novel Method for Understanding the Mixing Mechanisms to Enable Sustainable Manufacturing of Bioinspired Silica.

Bioinspired silica (BIS) has received unmatched attention in recent times owing to its green synthesis, which offers a scalable, sustainable, and economical method to produce high-value silica for a wide range of applications, including catalysis, environmental remediation, biomedical, and energy storage. To scale-up BIS synthesis, it is critically important to understand how mixing affects the reaction at different scales. In particular, successful scale-up can be achieved if mixing time is measured, modeled, and kept constant across different production scales. To this end, a new image analysis technique was developed using pH, as one of the key parameters, to monitor the reaction and the mixing. Specifically, the technique involved image analysis of color (pH) change using a custom-written algorithm to produce a detailed pH map. The degree of mixing and mixing time were determined from this analysis for different impeller speeds and feed injection locations. Cross validation of the mean pH of selected frames with measurements using a pH calibration demonstrated the reliability of the image processing technique. The results suggest that the bioinspired silica formation is controlled by meso- and, to a lesser extent, micromixing. Based on the new data from this investigation, a mixing time correlation is developed as a function of Reynolds number-the first of a kind for green nanomaterials. Further, we correlated the effects of mixing conditions on the reaction and the product. These results provide valuable insights into the scale-up to enable sustainable manufacturing of BIS and other nanomaterials.

Chemistry of aqueous silica nanoparticle surfaces and the mechanism of selective peptide adsorption.

Control over selective recognition of biomolecules on inorganic nanoparticles is a major challenge for the synthesis of new catalysts, functional carriers for therapeutics, and assembly of renewable biobased materials. We found low sequence similarity among sequences of peptides strongly attracted to amorphous silica nanoparticles of various size (15-450 nm) using combinatorial phage display methods. Characterization of the surface by acid base titrations and zeta potential measurements revealed that the acidity of the silica particles increased with larger particle size, corresponding to between 5% and 20% ionization of silanol groups at pH 7. The wide range of surface ionization results in the attraction of increasingly basic peptides to increasingly acidic nanoparticles, along with major changes in the aqueous interfacial layer as seen in molecular dynamics simulation. We identified the mechanism of peptide adsorption using binding assays, zeta potential measurements, IR spectra, and molecular simulations of the purified peptides (without phage) in contact with uniformly sized silica particles. Positively charged peptides are strongly attracted to anionic silica surfaces by ion pairing of protonated N-termini, Lys side chains, and Arg side chains with negatively charged siloxide groups. Further, attraction of the peptides to the surface involves hydrogen bonds between polar groups in the peptide with silanol and siloxide groups on the silica surface, as well as ion-dipole, dipole-dipole, and van-der-Waals interactions. Electrostatic attraction between peptides and particle surfaces is supported by neutralization of zeta potentials, an inverse correlation between the required peptide concentration for measurable adsorption and the peptide pI, and proximity of cationic groups to the surface in the computation. The importance of hydrogen bonds and polar interactions is supported by adsorption of noncationic peptides containing Ser, His, and Asp residues, including the formation of multilayers. We also demonstrate tuning of interfacial interactions using mutant peptides with an excellent correlation between adsorption measurements, zeta potentials, computed adsorption energies, and the proposed binding mechanism. Follow-on questions about the relation between peptide adsorption on silica nanoparticles and mineralization of silica from peptide-stabilized precursors are raised.

Removal of formaldehyde from air using functionalized silica supports.

This paper demonstrates the use of functionalized meso-silica materials (MCM-41 or SBA-15) as adsorbents for formaldehyde (H2CO) vapor from contaminated air. Additionally new green nanosilica (GNs) materials were prepared via a bioinspired synthesis route and were assessed for removal of H2CO from contaminated indoor air. These exciting new materials were prepared via rapid, 15 min, environmentally friendly synthesis routes avoiding any secondary pollution. They provided an excellent platform for functionalization and extraction of H2CO demonstrating similar performance to the conventional meso-silica materials. To the authors' knowledge this is the first reported practical application of this material type. Prior to trapping, all materials were functionalized with amino-propyl groups which led to chemisorption of H2CO; removing it permanently from air. No retention of H2CO was achieved with nonfunctionalized material and it was observed that best extraction performance required a dynamic adsorption setup when compared to passive application. These results demonstrate the first application of GNs as potential adsorbents and functionalized meso-silica for use in remediation of air pollution in indoor air.

Recent Advances in Enabling Green Manufacture of Functional Nanomaterials: A Case Study of Bioinspired Silica.

Global specialty silica production is over 3 million tonnes per annum with diverse applications across sectors and an increasing demand for more complex material structures and surface chemistries. Commercial manufacturing of high-value silica nanomaterials is energy and resource intensive. In order to meet market needs and mitigate environmental impacts, new synthesis methods for these porous materials are required. The development of the bioinspired silica (BIS) product system, which is the focus of this review, provides a potential solution to this challenge. BIS is a versatile and greener route with the prospect of good scalability, attractive process economics and well controlled product materials. The potential of the system lies not only in its provision of specific lead materials but also, as itself, a rich design-space for the flexible and potentially predictive design of diverse sustainable silica nanomaterials. Realizing the potential of this design space, requires an integrative mind-set, which enables parallel and responsive progression of multiple and dependent research strands, according to need, opportunities, and emergent knowledge. Specifically, this requires development of detailed understanding of (i) the pathways and extent of material diversity and control, (ii) the influences and mechanisms of scale-up, and (iii) performance, economic and environmental characteristics and sensitivities. Crucially, these need to be developed for the system overall, which sits in contrast to a more traditional research approach, which focuses initially on the discovery of specific material leads at the laboratory scale, leaving scale-up, commercialization, and, potentially, pathway understanding to be considered as distinctly separate concerns. The intention of this review is to present important recent advances made in the field of BIS. Specifically, advances made along three research themes will be discussed: (a) particle formation pathways, (b) product design, and (c) scale-up and manufacture. These advances include first quantitative investigation of synthesis-product relationships, first structured investigation of mixing effects, preparation of a broad range of functionalized and encapsulated silica materials and continued industrial engagement and market research. We identify future challenges and provide an important foundation for the development of new research avenues. These include the need to develop comprehensive and predictive product design models, to understand markets in terms of product cost, performance and environmental considerations, and to develop capabilities enabling rapid prototyping and scale-up of desired nanomaterials.

Facile Cellulase Immobilisation on Bioinspired Silica.

Cellulases are enzymes with great potential for converting biomass to biofuels for sustainable energy. However, their commercial use is limited by their costs and low reusability. Therefore, the scientific and industrial sectors are focusing on finding better strategies to reuse enzymes and improve their performance. In this work, cellulase from Aspergillus niger was immobilised through in situ entrapment and adsorption on bio-inspired silica (BIS) supports. To the best of our knowledge, this green effect strategy has never been applied for cellulase into BIS. In situ entrapment was performed during support synthesis, applying a one-pot approach at mild conditions (room temperature, pH 7, and water solvent), while adsorption was performed after support formation. The loading efficiency was investigated on different immobilisation systems by Bradford assay and FTIR. Bovine serum albumin (BSA) was chosen as a control to optimize cellulase loading. The residual activity of cellulase was analysed by the dinitro salicylic acid (DNS) method. Activity of 90% was observed for the entrapped enzyme, while activity of ~55% was observed for the adsorbed enzyme. Moreover, the supported enzyme systems were recycled five times to evaluate their reuse potential. The thermal and pH stability tests suggested that both entrapment and adsorption strategies can increase enzyme activity. The results highlight that the entrapment in BIS is a potentially useful strategy to easily immobilise enzymes, while preserving their stability and recycle potential.

A Comparison of Environmental Impact of Various Silicas Using a Green Chemistry Evaluator.

To answer questions surrounding the sustainability of silica production, MilliporeSigma's DOZN 2.0 Green Chemistry Evaluator was employed as it provides quantitative values based on the 12 principles of Green Chemistry. As a first study using DOZN 2.0 to evaluate the greenness of nanomaterials, a range of silica types were considered and their greenness scores compared. These included low- and high-value silicas, both commercial and emerging, such as precipitated, gel, fumed, colloidal, mesoporous, and bioinspired silicas. When surveying these different types of silicas, it became clear that while low value silicas have excellent greenness scores, high-value silicas perform poorly on this scale. This highlighted the tension between high-value silicas that are desired for emerging markets and the sustainability of their synthesis. The calculations were able to quantify the issues pertaining to the energy-intensive reactions and subsequent removal of soft templates for the sol-gel processes. The importance of avoiding problematic solvents during processes and particularly releasing them as waste was identified. The calculations were also able to compare the amount of waste generated as well as their hazardous nature. The effects of synthesis conditions on greenness scores were also investigated in order to better understand the relationship between the production process and their sustainability.

Interactions of amines with silicon species in undersaturated solutions leads to dissolution and/or precipitation of silica.

The biogeochemical silicon cycle is the focus for many researchers studying the dissolution of silicon species from quartz, amorphous, and biogenic silica. Furthermore, the precipitation of biogenic silica by diatoms, radiolarian, sponges, and plants is also a popular focus for research. The ornate silica structures created by these species has attracted interest from biomaterial scientists and biochemists who have studied mineral formation in an attempt to understand how biogenic silica is formed, often in the presence of proteins and long chain polyamines. This article is at the interface of these seemingly distinct research areas. Here we investigate the effect of a range of amines in globally undersaturated silicon environments. Results are presented on the effect of amine-containing molecules on the formation of silica from undersaturated solutions of orthosilicic acid and globally undersaturated silicon environments. We sought to address two questions: can silica be precipitated/harvested from undersaturated solutions, and can we identify the silicon species that are most active in silica formation? We demonstrate that none of the bioinspired additives investigated here (e.g., poly(allylamine hydrochloride), pentaethylenehexamine, and propylamines) have any influence on orthosilicic acid at undersaturated concentrations. However, under globally undersaturated silicon concentrations, small molecules and polymers containing amine groups were able to interact with oligomers of silicic acid to either generate aggregated materials that can be isolated from solution or increase rates of oligomer dissolution back to orthosilicic acid. Additional outcomes of this study include an extended understanding of how polyelectrolytes and small molecules can promote and/or inhibit silica dissolution and a new method to explore how (bio)organic molecules interact with a forming mineral phase.

Poly(vinyl amine)-silica composite nanoparticles: models of the silicic acid cytoplasmic pool and as a silica precursor for composite materials formation.

The role of polymer (poly(vinylamine)) size (238-11000 units) on silicic acid condensation to yield soluble nanoparticles or composite precipitates has been explored by a combination of light scattering (static and dynamic), laser ablation combined with aerosol spectrometry, IR spectroscopy, and electron microscopy. Soluble nanoparticles or composite precipitates are formed according to the degree of polymerization of the organic polymer and pH. Nanoparticles prepared in the presence of the highest molecular weight polymers have core-shell like structures with dense silica cores. Composite particles formed in the presence of polymers with extent of polymerization below 1000 consist of associates of several polymer-silica nanoparticles. The mechanism of stabilization of the "soluble" silica particles in the tens of nanometer size range involves cooperative interactions with the polymer chains which varies according to chain length and pH. An example of the use of such polymer-poly(silicic acid) nanoparticles in the generation of composite polymeric materials is presented. The results obtained have relevance to the biomimetic design of new composite materials based on silica and polymers and to increasing our understanding of how silica may be manipulated (stored) in the biological environment prior to the formation of stable mineralized structures. We suspect that a similar method of storing silicic acid in an active state is used in silicifying organisms, at least in diatom algae.

From biosilicification to tailored materials: optimizing hydrophobic domains and resistance to protonation of polyamines.

Considerable research has been directed toward identifying the mechanisms involved in biosilicification to understand and possibly mimic the process for the production of superior silica-based materials while simultaneously minimizing pollution and energy costs. Molecules isolated from diatoms and, most recently sponges, thought to be key to this process contain polyamines with a propylamine backbone and variable levels of methylation. In a chemical approach to understanding the role of amine (especially propylamine) structures in silicification we have explored three key structural features: (i) the degree of polymerization, (ii) the level of amine methylation, and (iii) the size of the amine chain spacers. In this article, we show that there are two factors critical to their function: the ability of the amines to produce microemulsions and the presence of charged and uncharged amine groups within a molecule, with the latter feature helping to catalyze silicic acid condensation by a proton donor/acceptor mechanism. The understanding of amine-silicate interactions obtained from this study has enabled the controlled preparation of hollow and nonporous siliceous materials under mild conditions (circumneutral pH, room temperature, and in all aqueous systems) possibly compatible with the conditions used by biosystems. The "rules" identified from our study were further used predictively to modulate the activity of a given amine. We believe that the outcomes of the present contribution will form the basis for an approach to controlling the growth of inorganic materials by using tailor-made organic molecules.

Exploiting nanoscale effects enables ultra-low temperature to produce porous silicon.

The magnesiothermic reduction (MgTR) of silica has been recently shown to produce porous silicon which can be used in applications such as photocatalysis and energy storage. MgTR typically requires ≥650 °C to achieve meaningful conversions. However, high temperatures are detrimental to the highly desired porosity of silicon, while also raising doubts over the sustainability of the process. In this work we show for the first time that the onset temperature of the MgTR is dependent on the particle size of the feedstock silica. Using both in-house synthesised and commercial silica, we have shown that only particles ≤20 nm are able to trigger the reaction at temperatures as low as 380 °C, well below a previously reported cut-off temperature of 500 °C, producing porous, crystalline silicon. The decrease in temperature requirement from ≥650 °C to 380 °C achieved with little modification to the overall process, without any additional downstream processing, presents significant implications for sustainable and economical manufacturing of porous silicon.

Enabling scale-up of mesoporous silicon for lithium-ion batteries: a systematic study of a thermal moderator.

The volume expansion of silicon during cycling of a lithium-ion battery (LIB) leads to degradation and capacity loss. Porous silicon can address many of the issues faced by silicon active materials and has previously been shown to have excellent cyclability. Recently we have uncovered the mechanisms underpinning the pore evolution in magnesiothermic reduction (MgTR) of silica and further demonstrated that it has the potential to produce porous silicon in a scalable and economic manner [J. Mater. Chem. A, 2020, 8, 4938]. However, the scalability of MgTR is affected by the large excess heat produced during reaction. Although previous studies have shown that NaCl can be used as a thermal moderator to mitigate this issue, this has not been systematically investigated, leading to a lack of knowledge on scalability of MgTR. Here, by carefully investigating the roles of NaCl, we show that the NaCl ratio and reduction temperature are the critical factors for controlling scale-up and the product properties. We identified the upper temperature limit of NaCl as a thermal moderator. Further, we systematically showed how the amount of NaCl and the reduction temperature affect the porous properties of the product silicon. Our results have established pathways for scaling-up this method such that it can now be taken forward to target specific porous silicon properties.