Materials Horizons Blog

For a considerable period, the engine of progress was fuelled primarily by economic incentives. However, this paradigm has shifted due to increased awareness of the environmental consequences of society. Focus has turned towards embracing sustainability as a precursor to assimilating the fruits of progress into industry. This trend leverages the conversion of different waste feedstock like plastics, metals, etc, into new added value matter. The added value matter could be fuels, solvents, organic substrates, new polymers, and functional materials.

Embracing the notion that “the new is often the well-forgotten old,” the use of biomass as a feedstock for materials with practicable qualities has been revisited and revitalized. The biomass is feedstock mainly derived from agricultural and forestry resources, and animal resources. Compared to other waste feedstock, such as plastic, electronic, and construction waste, biomass already fits more within the sustainable economic strategy due to its natural origin. The other side of this coin is the insufficient mechanical properties of biomass-derived materials which leads to poor durability and recyclability of functional materials from biomass.

Recent work from Leixiao Yu, Lingyan Gao, Shengyi Dong and team suggests a supramolecular strategy to overcome these limitations. They reported the conversion of 6 types of biomass (cellulose, guar gum, sericin protein, chitin, corn protein and potato starch) to functional materials via copolymerization with thioctic acid (TA) to afford poly[TA-biomass]. The material formation is driven by hydrogen bonding between TA and the polar functional groups in the biomass. Despite such non-covalent forces being reversible and inherently weaker than covalent bonds, prepared materials are proven to be highly impact resistant. The prepared poly[TA-biomass] is highly adhesive and water-resistant, however, it could be fully depolymerized by simple ethanol treatment and involved in the next cycle of polymerization-utilization without any obvious decay in mechanical strength. This anticipates potential applications of poly[TA-biomass as anti-water, impact resistant materials. The team expanded the potential application directions to the biomedical field by demonstrating high biocompatibility, nontoxicity, and antimicrobial effects towards both gram-positive and negative bacteria, attributed to TA. For instance, the newly prepared poly[TA-biomass] may hold promise for smart packaging or wound healing materials.

This recent work is a perfect example of the “waste to wealth” approach, where materials chemistry assisted in transforming common feedstock into functional materials. Combining waste feedstock with a supramolecular strategy is a promising concept that can be broadened to the use of other types of feedstock (plastic, metal) and a broad family of non-covalent interactions (hydrogen bonding, π- π stacking, hydrophobic effects). At the moment, however, this research directs the attention of the community to biomass as a promising feedstock for functional materials design.

To find out more, please read:

A supramolecular approach for converting renewable biomass into functional materials
Yunfei Zhang, Changyong Cai, Ke Xu, Xiao Yang, Leixiao Yu, Lingyan Gao and Shengyi Dong
Mater. Horiz., 2024, Advance Article, DOI: 10.1039/D3MH01692G

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About the blogger

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Dr Olga Guselnikova is a member of the Materials Horizons Community Board. She recently joined the Center for Electrochemistry and Surface Technology (Austria) to work on functional materials as a group leader. Dr. Guselnikova received her PhD degree in chemistry from the University of Chemistry and Technology Prague (Czech Republic) and Tomsk Polytechnic University (Russia) in 2019. Her research interests are related to surface chemistry for functional materials. This means that she is applying her background in organic chemistry to materials science: plasmonic and polymer surfaces are hybridized with organic molecules to create high-performance elements and devices.

As we progress towards sustainable sources of energy, achieving efficient hydrogen generation through water splitting becomes increasingly vital. However, the challenges associated with electrode and membrane degradation due to corrosion in seawater hinder large-scale applications. While indirect seawater splitting through pre-desalination can circumvent corrosion issues, it introduces a drawback demanding additional energy input, rendering it economically less attractive. Therefore, the development of cost-effective water splitting electrocatalysts is essential for making the overall electrolysis process economically viable for widespread adoption and industrialization. Metal oxide electrocatalysts with active site engineering is a cutting-edge strategy to obtain high activity and durable catalysts for long-lasting performance under harsh saline conditions.

Recent work by Meng and team presents heterogeneous spin state molybdenum dioxide (MoO₂) as a promising electrocatalyst to address the above critical challenges. Their novel approach involves the incorporation of Fe into MoO₂ nanosheets on Ni foam (Fe-MoO₂/NF) through a rapid carbothermal shocking method. This synthetic process facilitates the lattice dislocations, effectively exposing rich O vacancies and inducing a low-oxidation state in Mo sites, especially during the rapid Joule heating process. This results in the manipulation of spin states between Fe and Mo atoms. The resulting catalyst demonstrates remarkable performance for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline seawater.

The integration of heterogeneous spin states into MoO₂ disrupts the d–d orbital coupling resulting in modified electronic configuration. This significantly affects the binding energy between the active sites and reaction intermediates, thereby enhancing the electrocatalytic activity. The catalytic activity of Fe–MoO₂/NF is demonstrated by ultralow overpotentials recorded for both HER (17 mV@10 mA cm⁻²) and OER (310 mV@50 mA cm⁻²). Furthermore, the catalyst exhibits high selectivity in alkaline seawater splitting, showcasing its potential for efficient hydrogen production in challenging environmental conditions. To demonstrate the practical applicability of this newly developed electrocatalyst, the Fe–MoO₂/NF is assembled into an anion exchange membrane seawater electrolyser that achieves a low energy consumption of 5.5 kW h m⁻³, emphasizing its practical application in renewable energy systems.

An important aspect of this research is the successful coupling of Fe–MoO₂/NF with a solar-driven electrolytic system, yielding a solar-to-hydrogen efficiency of 13.5%. This demonstrates the catalyst’s compatibility with solar energy, opening avenues for sustainable and clean hydrogen production. Theoretical insights into the electronic structure of Fe-incorporated MoO₂, along with the abundance of oxygen vacancies, provides a deeper understanding of the catalytic mechanisms involved. Distortion of the Mo–O bonds, optimized through this method, plays a crucial role in enhancing the binding energy of adsorbed species during the electrochemical processes.

Looking forward, these findings hold significant promise for practical water splitting at a scalable level, especially in the context of solar-to-hydrogen production. The successful integration of Fe–MoO₂/NF into a solar-driven electrolytic system is a critical step toward sustainable and eco-friendly widespread adoption and integration into large-scale hydrogen production systems. In the pursuit of practical water splitting for solar-to-hydrogen production on a larger scale, the key challenge lies in making the process economically viable and technologically feasible. Thus, the prospects for this work include optimizing the synthesis method and extending for other heterogeneous spin such as Ni and Co for seawater splitting, which might extend the versatility of the proposed strategy, offering a simple and efficient approach for efficient electrocatalysts. The simplicity and efficiency of the proposed strategy make it an attractive option for large-scale implementation. As the demand for green energy solutions and the reduction of carbon footprints continue to grow, the significance of advancements in water-splitting, especially saline water electrolysis driven by solar energy, cannot be overstated. This study adds to the growing body of evidence that renewable energy sources can be used to produce hydrogen, which will pave the ways towards a more greener and sustainable energy future.

To find out more, please read:

Rapid carbothermal shocking fabrication of iron-incorporated molybdenum oxide with heterogeneous spin states for enhanced overall water/seawater splitting
Jianpeng Sun, Shiyu Qin, Zhan Zhao, Zisheng Zhang and Xiangchao Meng
Mater. Horiz., 2024, Advance Article, DOI: 10.1039/D3MH01757E

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About the blogger

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Shahid Zaman is currently a postdoctoral fellow at Hydrogen Research Institute, University of Quebec Trois-Rivières, Canada, and he is a Materials Horizons Community Board member. He received his Ph.D. in Material Physics and Chemistry from Huazhong University of Science and Technology in 2021. From 2021 to 2023, he worked as a postdoctoral fellow at the Southern University of Science and Technology, China. Dr. Shahid’s current research interests are nanomaterials for electrocatalysis in proton exchange membrane fuel cells and water electrolyzers.

Peripheral nerve injury is a common complication of surgical procedures and traumatic insults that can result in severe discomfort including chronic pain and sensory defects. Ensuring that the injured nerve regenerates is therefore critical for the long-term well-being of affected individuals. A promising therapeutic strategy to enhance regeneration is to target both the initial inflammation and subsequently promote axonal regrowth. How to deliver drugs sequentially and on time for each patient is a complex engineering challenge requiring innovative solutions.

Toward this end, Shan and the team developed a stimuli-responsive drug delivery scaffold for a dual drug delivery that makes it possible to target both inflammation and axonal sprouting sequentially. The team created a composite material that consists of a Poly-L-lactic acid (PLLA) shell that provides mechanical support to the regenerating axons and that can be loaded with the novel bioactive and stimuli-responsive scaffold.

To achieve a staggered drug release profile, the team encapsulated vitamin B12 (vB12) with anti-inflammatory properties directly inside calcium crosslinked alginate hydrogel and used an additional multilevel encapsulation approach consisting of microspheres loaded with nerve growth factor (NGF)-adsorbed nanoparticles enabling their delayed release compared to vB12. The team then leveraged ultrasound as a stimulus to hierarchically open polymer chains in ultrasound-responsive calcium cross-linked alginate hydrogels.  The rate of release of both vB12 and NGF was not predetermined and could be tuned by changing ultrasound intensity thus enabling the adjustment of the delivery profiles based on the individual patient’s healing progress. Interestingly, the ultrasound treatment alone improved peripheral nerve regeneration by promoting the secretion of neurotrophins in rodent models. Taken together, Shan and colleagues developed a novel strategy to promote nerve repair which utilizes ultrasound as an easily administrable stimulus that makes it possible to adjust the treatment of injured peripheral nerves based on patient’s needs.

To find out more, please read:

A responsive cascade drug delivery scaffold adapted to the therapeutic time window for peripheral nerve injury repair
Yizhu Shan, Lingling Xu, Xi Cui, Engui Wang, Fengying Jiang, Jiaxuan Li, Han Ouyang, Tailang Yin, Hongqing Feng, Dan Luo, Yan Zhang and Zhou Li
Mater. Horiz., 2024, Advance Article, DOI: 10.1039/D3MH01511D

 

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About the blogger

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Anna Stejskalova is currently a postdoctoral fellow at the Wyss Institute at Harvard Medical School and a member of the Materials Horizons Community Board. Dr. Stejskalova’s research focuses on female reproductive health and preterm birth utilizing organs on a chips and advanced biomaterials.

Quantitative prediction of material function based on multi-scale modeling is of vital importance for not only systematic performance optimization, but also precise materials design. Due to the nonintuitive and nontrivial structure-property relationship across different length scales, a comprehensive characterization of the hierarchical behavior of functional materials still remains a formidable challenge to date. To deal with this burning issue, recently Hernandez et al. made full use of data science techniques and developed an interpretable neural network architecture, viz. NestedAE, to link and quantify material properties across various length scales.

In NestedAE, an autoencoder is used to represent each physical scale of the materials, and a series of autoencoders are connected. Thus, the successive transfer of ‘‘important’’ information from one scale to another can be realized by the latent space of each autoencoder. In contrast to the previous approaches, in NestedAE each autoencoder possesses a different architecture and it is trained upon its own data set. Moreover, both the latents from the previous autoencoder and the features from the data set are reconstructed by the autoencoder.

To demonstrate the applicability of this newly developed machine learning architecture, Hernandez et al. employed NestedAE to compute the density-functional-theory bandgaps of metal halide perovskites based on their atomic and ionic properties. Furthermore, their power conversion efficiencies were also predicted. It was proven that the predicted results agreed well with the previous experimental observations, and its application on the metal halide perovskites established the correlation between the fundamental atomistic-level structural properties and the macroscopic device performance.

In summary, this computational study developed an interpretable machine learning architecture, NestedAE, to quantitatively predict the material properties at many length scales and to correlate the basic chemical structure and the macroscopic performance. These insightful results pioneer a new way for hierarchically optimizing and designing new functional materials.

To find out more, please read:

NestedAE: interpretable nested autoencoders for multi-scale materials characterization
Nikhil Thota, Maitreyee Sharma Priyadarshini, and Rigoberto Hernandez
Mater. Horiz., 2024, Advance article, DOI: 10.1039/D3MH01484C

 

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About the blogger

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Wen Shi is currently an Associate Professor at the School of Chemistry, Sun Yat-sen University and he is a Materials Horizons Community Board member. He received his Ph.D. in physical chemistry from Tsinghua University in 2017. From 2017 to 2021, he worked as a scientist at Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR) in Singapore. Dr. Shi’s current research interests are in theoretical computations and simulations of functional materials.

Developing low-cost, earth-abundant catalysts is essential in the quest for green hydrogen production through electrochemical water splitting. However, screening and optimizing the performance of these materials has traditionally been a time-consuming and resource-intensive process. Integrating innovative approaches such as machine learning in electrocatalysis presents a promising solution to expedite catalyst screening and discovery. Could these strategies be the game-changer in electrocatalysis design and testing? A recent study by Lim et al. suggests that combining machine learning with lab automation is ideal for identifying effective catalysts for hydrogen and oxygen evolution reactions.

The central figures of this study are transition metal layered double hydroxide (LDH) catalysts known for their unique lamellar structure and tunable chemical compositions. By examining five metal components (Ni, Co, Fe, Mo, and W) at varying ratios, the study efficiently leveraged machine learning to explore different compositions and correlate experimental electrochemical performance. This approach followed a simple yet powerful machine learning optimization workflow (Figure 1) that utilized a small initial dataset, Bayesian Optimization, and three machine learning algorithms: the Gaussian Process Regression, Gradient Boosting, and Neural Networks.

The neural networks proved most effective in predicting optimal catalyst compositions. The champion catalyst emerged as the molybdate-intercalated CoFe LDH, exhibiting overpotentials of 266 and 272 mV for the oxygen and hydrogen evolution reactions, respectively, while maintaining a decent stability over 50 hours. What makes this particular combination stand out? Integrating molybdate is thought to disrupt the LDH’s turbostratic structure, thereby increasing the number of active sites. Interestingly, the study also noted an unexpected outcome: Ni, typically a critical component in high-performing water-splitting electrocatalysts, was frequently excluded by the model’s recommendations. Why does this occur? It is time for the electrocatalysis detectives to investigate!

An automated synthesis system also provided an effective platform for scaling up these materials without significantly altering their physical and chemical properties. This aspect highlights the potential for industrial application and sets a precedent for scaling up electrocatalytic materials in the field.

In summary, this study underscores the potential of integrating machine learning methods into experimental workflows. This approach expedites the optimization of electrocatalysis performance, marking a substantial advancement in developing efficient and sustainable hydrogen production technologies.

To find out more, check out the full publication:

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting
Carina Yi Jing Lim, Riko I Made, Zi Hui Jonathan Khoo, Chee Koon Ng, Yang Bai, Jianbiao Wang, Gaoliang Yang, Albertus D. Handoko, and Yee-Fun Lim
Mater. Horiz., 2023,10, 5022-5031 DOI: 10.1039/D3MH00788J

 

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About the blogger

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Raul A. Marquez is a Chemistry Ph.D. student working with Prof. C. Buddie Mullins at The University of Texas at Austin and a Materials Horizons Community Board member. His research focuses on understanding the chemical transformations of electrocatalytic materials and developing functional devices for energy storage and conversion technologies. Follow Raul’s latest research by following him on X (formerly Twitter): @ruloufo

Additive manufacturing is a polymer processing method enabling the preparation of 3D architectures with a high level of design freedom. While some of the additive manufacturing technologies, such as fused deposition modeling (FDM) are commonly used at an industrial level for prototyping, there are still numerous challenges to tackle for achieving a 3D architecture that possesses state-of-the-art thermomechanical properties, compared to those obtained via conventional methods. Considering the sustainability aspect of the selected additive manufacturing method, including the management of the failed 3D parts, is also of utmost importance in the context of sustainable laboratories.

As recently reported by Jiang et al., there are additional challenges for the additive manufacturing of continuous fiber composites which are made of a carbon fiber that is surrounded by a polymer matrix. FDM is often used to prepare such samples, where the continuous carbon fiber and the thermoplastic filament are fed separately during the processing of the materials. While this strategy works to produce continuous fiber composites, the resulting mechanical properties are mostly dictated by those of the thermoplastic polymer. To optimize the mechanical properties of such samples, an interesting alternative strategy consists of using a thermoset polymer matrix processed via direct-ink writing (DIW). While some successes have been reported exploiting DIW, the rheological properties of the thermoset in the pre-printing stage needs to meet specific requirements to enable the extrusion of the formulation, including shear-thinning and thixotropy. The solidification kinetics of the thermoset formulation occurring upon exposure of the 3D printed formulation to heat is usually slow and leads to a uniform curing, but also leads to difficulties in obtaining a 3D printed architecture with a high level of post-treatment print fidelity. To circumvent this problem, UV curable resins, once again printed via direct-ink writing, can be used as a polymer matrix for the continuous fiber composite as their solidification kinetics are often faster than those of thermosets. While this process is efficient, the presence of the continuous fiber may impede the penetration of the irradiation, usually leading to a fast yet non-uniform curing.

To address the challenges involved in the preparation of continuous fiber composites linked to the DIW of either the thermoset or the UV curable resin, Jiang et al. designed a formulation capable of undergoing a two-stage curing process, therefore successfully combining the advantages of both UV curing (fast solidification) and heat-based curing (uniform curing). They combined the 2-hydroxy-3-phenoxypropyl acrylate monomer, the phenylbis (2,4,6-glycerolate diacrylate) photoinitiator, and a triazabicyclodecene as the bond exchange reaction catalyst. As illustrated in Figure 1, the first stage (UV irradiation) allows for the free-radical polymerization to occur while the second stage (heat) is used to increase the resulting material’s crosslinking density via the transesterification reactions occurring between the hydroxyl and the ester functional groups of the material.

This transesterification, also referred to as bond exchange reaction, is crucial to optimize the thermomechanical properties of the 3D printed continuous fiber composites, which results in high performance applications for these architectures. Jiang et al. reported not only a ~11-fold increase in the modulus of the two-stage cured samples compared to the UV cured only samples, but also a better adhesion (referred to as welding) between the layers deposited on top of one another. This enhanced adhesion is a consequence of the covalent bond created between the layers upon the heating step which is, once again, facilitated by the bond exchange reaction.

The capability of the 3D printed architectures to undergo bond exchange reaction also allows for the repairing and reshaping of the architectures. It was shown that the 3D printed architectures could be recycled via depolymerization in ethylene glycol at high temperature (160°C), which is an important asset for a thermoset based composite, especially in the context of sustainable materials and processing. This proof-of-concept has been extended to acrylate/epoxy-based commercial resins, opening the door to fundamental studies of the mechanisms of bond exchange reactions in similar resins where further understanding of the structure-processing-property relationships could be established to lead to the rational design of custom resins for the 3D printing of continuous fiber composites.

To find out more, please read:

3D Printing of continuous fiber composites using two-stage UV curable resin
Huan Jiang, Arif M. Abdullah, Yuchen Ding, Christopher Chung, Martin L. Dunn and Kai Yu
Mater. Horiz., 2023, 10, 5508-5520, DOI: 10.1039/D3MH01304A

 

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About the blogger

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Audrey Laventure is an assistant professor in the Department of Chemistry at the Université de Montréal (UdeM), QC, Canada, and a member of the Materials Horizons Community Board. Since 2021, she holds the Canada Research Chair in Functional Polymer Materials. Her expertise lies at the intersection of physical chemistry, polymer processing and advanced materials characterization. In 2023, Audrey was selected to lead the molecular materials axis of the new Institut Courtois at UdeM. Audrey was also part of the first Youth Council of the Chief Science Advisor of Canada (2020-2023) and the Science Meets Parliament 2023 cohort.