Materials Horizons Blog

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.

Figure 1: (A) Synthesis scheme of Fe–MoO2 /NF; SEM and TEM image along with HRTEM images and Schematic representations of lattice distortion formation mechanism of incorporated heterogeneous spin state (B) DFT analysis of the optimized models of MoO2, Fe–MoO2 and charge density difference plot of Fe–MoO2 (C) HER, OER and AEM electrolyzer polarization curves in saline water with long-term HER stability measurements in alkaline seawater solution. Reproduced from DOI: 10.1039/D3MH01757E with permission from the Royal Society of Chemistry.

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

About the blogger

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.

Figure 1. Schematic illustration of the structure and drug release process of the responsive cascade drug delivery scaffold (RCDDS) for peripheral nerve injury repair. (A) A brief illustration of the structure of the RCDDS implanted in SD rat. (B) Drug release process of the RCDDS and the corresponding repair stage. Vitamin B12 loaded in the hydrogel system can be adjustably released in the early stage by ultrasound to alleviate inflammation, while NGF loaded in alginate microspheres and PLGA nanoparticles can be gradually released from the RCDDS to promote axon regeneration one month after implantation. Reproduced from DOI: 10.1039/D3MH01511D with permission from the Royal Society of Chemistry

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

About the blogger

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.

Figure 1: Unsupervised (A) and supervised (B) NestedAE architecture. Reproduced from DOI: 10.1039/d3mh01484c with permission from the Royal Society of Chemistry

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

About the blogger

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.