Materials Horizons Blog

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.

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.

Figure 1: Summary of machine learning optimization workflow. Reproduced from DOI: 10.1039/D3MH00788J with permission from the Royal Society of Chemistry.

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

About the blogger

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.

Figure 1. a) Chemical structures composing the two-stage curable resin undergoing b) UV curing and c) heating illustrating the bond exchange reaction involved in the optimization of the thermomechanical properties of the 3D printed architectures. Reproduced from DOI: 10.1039/d3mh01304a with permission from the Royal Society of Chemistry.

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

About the blogger

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.

The advantages of minimal invasiveness, excellent biocompatibility, and high spatiotemporal control manners have enabled photodynamic therapy (PDT) to be utilized as a novel alternative to conventional cancer treatment approaches. PDT relies on photosensitizers (PSs) to photochemically react with the ground-state oxygen molecules (oxygen, ³O₂) or small molecules, generating highly toxic reactive oxygen species (ROS) under light irradiation in situ, to further induce tumor cell apoptosis or necrosis, vascular damage or cancer-mediated immunity, for example.

Since the first discovery of PSs based on hematoporphyrin derivatives (HpD) in 1960, the porphyrin-based PSs and their PDT performance have been extensively explored. Currently, various porphyrin-based PSs with different structures such as verteporfin, porfimer sodium, temoporfin and photocarcinosin have been approved for clinical practice. Despite great progress in clinical practice, the ROS generation of these porphyrin-based PSs is still far from satisfactory. One of the main issues is their large planar and rigid structures, which tend to form tight aggregates with strong π…π interactions at high concentrations in aqueous solutions or at tumor tissues. Such π…π stacking will cause diminished fluorescence and compromised ROS, leading to low PDT efficacy.

To weaken π…π stacking of porphyrin-based PSs, boost the generation of ROS, and enhance the PDT efficacy, work by Zhu, Zhang et al recently reported a novel biocompatible pure organic porphyrin nanocage (Py-Cage) with significantly improved ROS generation and PDT performance. Their design of the Py-Cage highlights the large cavity and long distance which effectively weaken the π…π stacking effect of porphyrins within the nanocage. Hence this Py-Cage exhibits excellent anti-ACQ features at high concentrations in aqueous solution (Figure 1a,b,c).

Fig 1. (a) and (b) Schematic comparison between traditional planar porphyrin-based photosensitizers and the porous porphyrin nanocage in attenuating the ACQ effect. (c) Schematic illustration of the enhanced ROS generation for cationic organic nanocage Py-Cage. (d) Tumor pictures of 4T1 tumor-bearing mice after different treatments (for Py-cage samples). (e) Schematic of Py-Cage NPs synthesized by a nanoprecipitation method with DSPE-PEG2000 as the encapsulation matrix. (f) Tumor pictures of 4T1 tumor-bearing mice in different groups (for Py-cage NPs samples). Reproduced from DOI: 10.1039/D3MH01263H with permission from the Royal Society of Chemistry.

A systematic comparative investigation shows that the Py-Cage can largely boost ROS generation that is superior to its PyTtDy precursor as well as widely used PSs, including Chlorin E6 (Ce6) and Rose Bengal (RB). Having established the excellent ROS generation and bright fluorescence of the Py-Cage, the team then evaluated its in vitro PDT performance using mouse breast cancer 4T1 cell. The data shows the Py-Cage can generate a large amount of ROS in cells under white light irradiations to induce cell apoptosis and death. Encouraged by the very promising in vitro results, Zhu, Zhang et al. further conducted in vivo PDT trials of the Py-Cage using a 4T1 tumor bearing mouse model. Their findings indicate that the tumors in the Py-Cage+light group showed the smallest sizes and lowest tumor weights among all the tested groups (Figure 1d), reflecting the best tumor growth inhibition performance of Py-Cage under light. The biocompatibility of the Py-Cage was also investigated by analyzing the blood routine and biochemical parameters of the rats after 48 h injection through tail veins. All the results show that the Py-Cage does not cause infection and bleeding symptoms and no damage to the liver and kidney function was observed. Other parameters such as cholesterol (CHO), triglyceride (TG), high-density lipoprotein cholesterol (HDL), low-density lipoprotein (LDL), glucose level were also not affected by Py-Cage. Moreover, the team prepared the Py-Cage nanoparticles by a nano-precipitation method to improve its water disability and biocompatibility. Similar in vitro and in vivo PDT experiments with these Py-Cage NPs were conducted and the NPs also proved to be excellent in biocompatibility and PDT efficacy (Figure 1e,f).

In summary, this work demonstrated the first porphyrin-based pure porous organic nanocage (Py-Cage) with a large cavity volume to promote both type-I and type-II ROS generation. Through comprehensive in vitro and in vivo studies, the Py-Cage proved to be extremely powerful in PDT with excellent biocompatibility and enhanced anti-tumor efficacy. The design of Py-Cage with a pure organic porous skeleton could avoid the π…π stacking to fully utilize the excited triplet state of PSs to generate ROS. This design strategy will offer enormous prospects for preparing novel and effective PSs with excellent biocompatibility for PDT and related phototheranostic applications.

To find out more, please read:

A biocompatible pure organic porous nanocage for enhanced photodynamic therapy
Zhong-Hong Zhu, Di Zhang, Jian Chen, Hua-Hong Zou, Zhiqiang Ni, Yutong Yang, Yating Hu, Ruiyuan Liu, Guangxue Feng, Ben Zhong Tang
Mater. Horiz. 2023, 10, 4868-4881, DOI: 10.1039/D3MH01263H

About the blogger

Quan Li is currently a Professor at Tianjin University of Traditional Chinese Medicine and a member of the Materials Horizons Community Board. Prof. Li’s research lab focuses on the design and preparation of soft matter materials based on light-responsive molecular machines and Chinese herbal medicine for biomedical applications, including anti-cancer, skin disease treatment, and others.

Conjugated polymers (CPs) are transformative materials that have facilitated numerous advancements in the field of soft-matter electronics. Their low-cost, high structural tunability, and robust mechanical properties have made them desirable materials for broad range of applications, including in energy capture and storage, chemical and biological sensors, electronic skin, and electronic display devices. Recently, significant efforts have been made to develop intrinsically flexible and stretchable CPs and to understand the fundamental principles and structural characteristics that impart these elastomeric properties without impairment to charge transport. Central to this has been extensive characterization of the polymer morphology and microstructure, which yielded the discovery that local segmental order can facilitate efficient long-range charge transport in the amorphous domains of the polymer. However, directly probing the local segmental order in polymers and distinguishing the contributions of this domain towards the charge transport and physicochemical properties from that of the crystalline domains, which are defined by long-range ordering, remains challenging.

Now, a highly collaborative and extensive study by Luo et al. describes the development of a new technique for monitoring the subtle changes in the local segment order and amorphous fractions of the polymer microstructure by integrating Raman spectroscopy with fast-scanning calorimetry (FSC). The authors targeted a structurally diverse set of polymers to broadly classify their findings.

Figure 1. Modulating and probing microstructure of conjugated polymers by integrated ultrafast calorimetry and micro-Raman spectroscopy. Left: Schematic of this integrated technique. The time-temperature program used in this study was carried out by the chip sensor temperature controller. The growth of crystalline domain was identified by the evolution of melting peak collected through FSC. The degree of segmental order was analyzed by the Raman shift of C=C modes through resonant micro-Raman spectroscopy. Reproduced from DOI: 10.1039/D3MH00956D with permission from the Royal Society of Chemistry

Namely, analogs based on poly(3-hexylthiophene-2,5-diyl) (P3HT) and poly{2,2′-[(2,5-bis(2-hexyldecyl)-3,6-dioxo-2,3,5,6- tetrahydropyrrolo[3,4-c ]pyrrole-1,4-diyl)dithiophene]- 5,5′-diyl-alt-thiophen-2,5-diyl} (PDPP3T or PDPPT), which are prevalent throughout organic electronics. The polymers were first subjected to a carefully devised time-temperature program to erase the thermal history of the polymers by first subjecting the polymer samples to temperatures above their melting point (T_m). Following subsequent thermal quenching and annealing steps, the Raman and FSC measurements were recorded. By monitoring the evolution of the Raman spectra and tracking the shifts in C=C/C-N stretches with increasing annealing time, minute changes in the segmental order could be monitored. It was observed that the extent of segmental order saturates before maximum crystallinity is achieved and that the annealing temperature could be specifically tailored for the polymers to achieve a highly ordered microstructure with desired levels of crystallinity.

Next, polymer segmental order was correlated with the segmental dynamics and charge transport properties by using alternating current (ac) chip-calorimetry and fabricating organic field effect transistors (OFETs). It was found that the rigid amorphous fraction (RAF) plays a significant role in promoting segmental order, and that there is a strong correlation between the polymer segmental order and the OFET charge-carrier mobility. Overall, the findings, magnitude, and scope of this study makes it a pivotal work for the field of organic electronics, and it should have resounding impact throughout material science.

To find out more, read the full manuscript here:

Real-time correlation of crystallization and segmental order in conjugated polymers

Shaochuan Luo, Yukun Li, Nan Li, Zhiqiang Cao, Song Zhang, Michael U. Ocheje, Xiaodan Gu, Simon Rondeau-Gagné, Gi Xue, Sihong Wang,  Dongshan Zhou and Jie Xu

Mater. Horiz., 2023, Advance Article, DOI: 10.1039/D3MH00956D

About the blogger

Robert M. Pankow is an Assistant Professor at The University of Texas at El Paso and a member of the Material Horizons Community Board. Dr. Pankow’s research focuses on conjugated polymer synthesis, sustainable chemistry, and organic electronics. You can follow him on X (formerly Twitter) @RobertPankow.