Ion track formation and porosity in InSb following 125 MeV and 150 MeV 107Ag swift heavy-ion irradiation are investigated as a function of ion fluence and irradiation angle. Rutherford backscattering spectrometry in channelling geometry reveals track radii of approximately 5 nm and 8 nm for 125 MeV and 150 MeV ion energies, respectively. The results indicate that the damage fraction saturates at approximately 90% amorphization, suggesting partial recrystallization inhibits complete amorphization. In addition to track formation, a porous structure develops at a fluence of 8.8 & times; 1012 ions/cm2, whereas no significant porosity is observed at lower fluences. Notably, the average pore diameter and pore density differ for the different irradiation parameters.

Abstract Rapid and highly selective sensing of ultra-low concentration protein biomarkers remains a critical challenge important for early disease diagnosis and monitoring. Here, we use conical SiO 2 nanopore-based biosensing for the rapid detection of heart-type fatty acid binding protein (H-FABP). Antibodies were covalently immobilized on the nanopore surface through siloxane chemistry. The functionalized asymmetric nanopores generate a characteristic rectifying current–voltage response, which shows a distinct shift upon binding to the target protein due to partial neutralization of the negatively charged pore surface. The sensor exhibits excellent sensitivity in the attomolar to nanomolar concentration range with a detection limit (LOD) of ∼0.4 aM. Furthermore, the platform exhibits high selectivity, distinguishing H-FABP from non-target proteins (HSA and Hb) at concentrations six orders of magnitude higher. We also demonstrate that nanopores can be regenerated using sodium hypochloride and O 2 plasma treatment, enabling repeated functionalization and reuse.

Narrow nanometer-sized damage trails created by swift heavy ions, so-called "ion tracks", reflect a material's response to intense local electronic excitation. Ion tracks are commonly described as cylindrical damage zones with circular cross-sections. This assumption largely results from limitations of current characterization techniques to resolve ion track morphologies with sufficient detail. Here we present measurements of the cross-sectional morphology of ion tracks with angstrom-level precision using synchrotron-based small-angle x-ray scattering. We analyzed the track shape in single-crystalline fluorapatite, tourmaline, and synthetic alpha-quartz irradiated with 185 MeV 197Au ions along different crystallographic directions. Our results reveal a clear anisotropy in the track cross-sections: while [0001]-oriented tracks have a largely circular cross-section, tracks along (1010) show a distinct anisotropy. This anisotropy cannot be explained solely by electronic energy loss, but instead reflects the influence of the intrinsic physical properties of the crystals. The track dimensions correlate with the elastic properties in different crystallographic directions showing smaller cross-sections along directions of higher elastic stiffness. Furthermore, crystals with predominantly covalent bonding and higher thermal conductivity exhibit significantly smaller tracks. These findings highlight how anisotropic physical characteristics of single-crystal govern ion track formation, providing insight into the interplay between irradiation effects and crystal anisotropy.

We demonstrate that solid-state nanopore sensing is a powerful single-molecule method for analyzing RNA conformational ensembles. As a model, we employed n-Tr20, a neuron-specific cytoplasmic tRNA$_{\mathrm{UCU}}<^>{\mathrm{Arg}}$, whose C50U mutation is associated with neurodegeneration in C57BL/6J mice. Maturation of the n-Tr20$<^>{\mathrm{C50U}}$ precursor is impaired as the mutation stabilizes a conformational ensemble different from the wild type. To gain insights into how this mutation engenders structural differences, we used solid-state nanopore sensing for the real-time identification of metastable conformers that are not easily observable by ensemble methods. Ion-current traces recorded using an 8 nm nanopore revealed broad contours of the conformational landscape of n-Tr20/n-Tr20$<^>\mathrm{C50U}$ $\pm$ Mg$<^>{2+}$. Additionally, cryo-electron microscopy analysis and small-angle X-ray scattering studies revealed structural plasticity consistent with the nanopore-sensing data. Since dynamics undergird RNA (dys)function in cellular physiology and pathology, nanopore sensing to determine RNA conformational sampling is a valuable addition to the growing RNA structural analysis toolkit.

Conical nanopores in amorphous SiO2 thin films fabricated using the ion track etching technique show promising potential for filtration, sensing, and nanofluidic applications. The characterization of the pore morphology and size distribution, along with its dependence on the material properties and fabrication parameters, is crucial to designing nanopore systems for specific applications. Here, we present a comprehensive study of track-etched nanopores in thermal and plasma-enhanced chemical vapor-deposited (PECVD) SiO2 using synchrotron-based small-angle X-ray scattering (SAXS). The nanopores were fabricated by irradiating the samples with 89 MeV, 185 MeV, and 1.6 GeV Au ions, followed by hydrofluoric acid etching. We present a new approach for analyzing the complex highly anisotropic two-dimensional SAXS patterns of the pores by reducing the analysis to two orthogonal one-dimensional slices of the data. The simultaneous fit of the data enables an accurate determination of the pore geometry and size distribution. The analysis reveals substantial differences between the nanopores in thermal and PECVD SiO2. The track-to-bulk etching rate ratio is significantly different for the two materials, producing nanopores with cone angles that differ by almost a factor of two. Furthermore, thermal SiO2 exhibits an exceptionally narrow size distribution of only 2–4%, while PECVD SiO2 shows a higher variation ranging from 8% to 18%. The impact of different ion energies on the size of the nanopores was also investigated for pores in PECVD SiO2 and shows only negligible influence. These findings provide crucial insights for the controlled fabrication of conical nanopores in different materials, which is essential for optimizing membrane performance in applications that require precise pore geometry.

Membrane-based charge-selective separation is emerging as an excellent platform for the separation of biomolecules and nanoparticles. For efficient charge-selective molecular separation, thin membranes with a well-defined surface charge and a narrow pore size distribution are desirable. However, existing membrane technologies often struggle to achieve such a high performance. This work demonstrates the use of uniform conical nanopores in SiO2 membranes as a charge-based molecular separation platform. The conical nanopores were fabricated by using the ion-track etching technique. The native negative surface charge of the SiO2 membrane can be altered to a positive charge by attaching an aminosilane moiety. Our separation experiments demonstrate excellent separation efficiencies of molecules based on their charge. Negatively charged nanopore membranes transport positively charged molecules up to 36 times more efficiently than negatively charged molecules. In contrast, positively charged membranes transport negatively charged molecules approximately 20 times more efficiently than positively charged ones. Furthermore, these membranes demonstrate effective charge-based molecular separation capabilities, successfully isolating oppositely charged molecules from mixed solutions.

We have irradiated YBa $_{2}$ Cu $_{3}$ O $_{6+x}$ (YBCO) films without artificial pinning sites with Ag $^+$ ions with energies of 75 MeV and 150 MeV and fluences between 2–8 $\cdot 10^{11}$ ions/cm $^{2}$ in order to create as controlled nanorod pinning sites as possible. The structural and superconducting properties were determined before and after the irradiation with x-ray diffraction and magnetic measurement. After the irradiation also transport and transmission electron microscopy measurements were made. It was noted that the ion tracks are all parallel to the YBCO $c$ -axis of the sample and those done with 150 MeV ions formed continuous 5 nm diameter tracks, whereas with 75 MeV ions, the tracks were not continuous through the sample. The $T_{\mathrm{c}}$ and $J_{\mathrm{c}}$ $(0 \mathrm{\,T})$ decreased with the irradiation, but the in-field $J_{\mathrm{c}}$ increased. The maximum increase was obtained with the 150MeV and 4 $\cdot 10^{11}$ ions/cm $^{2}$ sample with continuous rods, where the distance between the rods was closest to the diameter of the rods. Thus, the previous theoretical models predicting optimal pinning when the pinning site diameter is approximately equal to the distance between the pinning sites, are experimentally verified for these very pure samples, with no other external pinning sites.

The success of a nanopore experiment relies not only on the quality of the experimental design but also on the performance of the analysis program utilized to decipher the ionic perturbations necessary for understanding the fundamental molecular intricacies. We have developed a data extraction and analysis framework that leverages parallel computing, efficient memory management (minimizing data aggregation), and vectorization, yielding significant performance enhancement. The open-seek-read-close data loading architecture running on multiple cores underpins the swift analysis of large files where an ~ ×18 improvement was found for a 100-minute-long file (~4.5 GB in size) compared to the more traditional single (cell) array data loading method. The proposed application was benchmarked against five other analysis platforms showcasing significant performance enhancement (>×6 to ×1120). The integrated provisions for batch analysis enable concurrently analyzing multiple files, a crucial capability notably absent in most existing analysis platforms. The batch-analysis feature is particularly vital for high-bandwidth experiments, wherein data is distributed across several files rather than consolidated into a single large file. Furthermore, the application is equipped with multi-level data fitting based on abrupt changes in the waveform. The ability to condense the extracted events to a single file improves data portability (e.g., 16 GB file acquired at 200 kHz with 28,182 events reduces to 47.9 MB in size—343× reduction in size) and enable a multitude of post-analysis extraction to be done efficiently. In summary, the utilization of parallel computing, efficient memory management, and vectorized operations have led to a fast analysis platform that delivers significant performance enhancement, making it well-suited for multiple and sizeable nanopore data file analysis.

Perovskite solar cell (PSC) technology is a promising candidate for space applications because of its high power‐to‐weight ratio, low‐cost fabrication process, and good tolerance to high‐energy particle radiation. In this work, perovskite films and resultant high‐efficiency PSCs are assessed under 10 MeV proton radiation at fluences in the range 1e12–1e14 p cm −2 , which are equivalent to 1 to 100 years in geostationary orbit (GEO) without any shielding or cover. For the first time, void formation and material ablation are detected on perovskite films, indicating structural damage of the materials under the proton radiation. Furthermore, ions inside the devices especially Au and Pb ions are displaced to underlying layers under the proton bombardment. These lead to the degradation of PSCs to ≈89% of the initial performance (from 24.1% to 21.4%) at the highest dose. The experimental results are supported by previous simulation works with a good fit in all optoelectronic parameters. This study provides insights into the degradation mechanism of PSCs under proton radiation and paves the way for the utilization of PSCs in space applications.

With growing interest in solid-state nanopore sensing-a single-molecule technique capable of profiling a host of analyte classes-establishing facile and scalable approaches for fabricating molecular-size pores is becoming increasingly important. The introduction of nanopore fabrication by controlled breakdown (CBD) has transformed the economics and accessibility of nanopore fabrication. Here, we introduce the design of an Arduino-based, portable USB-powered CBD device, with an estimated cost of <150 USD, which is approximate to 10-100x cheaper than most commercial solutions, capable of fabricating single nanopores conducive for single molecule sensing experiments. We demonstrate the facile fabrication of 60 tailored nanopores (similar to 2.6-12.6 nm) with similar to 80% of the pores within 1 nm of the target diameter. Selected pores were then tested with double-stranded DNA, the canonical molecular ruler, demonstrating their performance for single-molecule sensing applications. The device is constructed with off-the-shelf readily available components and controlled using a highly customizable MATLAB application, which has capabilities encompassing pore fabrication, pore enlargement, and current-voltage acquisition for pore size estimation. When combined with a portable amplifier, this device also provides a fully portable sensing platform, an important step toward portable solid-state nanopore sensing applications.

We have used silver-ion irradiation and proton irradiation to produce point-like and spherical defects in REBa 2 Cu 3 O 7 coated conductors. We compare the resulting pinning landscape for optimized fluences and show that proton irradiation gives a slightly greater pinning enhancement at 20 K, but in the same samples silver irradiation gives significantly better pinning enhancement at 65 K. We attribute this to the relative sizes of the defects and to the distribution of defects resulting from the different ion collision rates.

Future Science OAAhead of Print CommentaryOpen AccessNanopore sensing and machine learning: future of biomarker analysis and disease detectionShankar Dutt, Buddini Karawdeniya, Yapa MNDY Bandara & Patrick KluthShankar Dutt *Author for correspondence: E-mail Address: shankar.dutt@anu.edu.auhttps://orcid.org/0000-0002-6814-070XDepartment of Materials Physics, Research School of Physics, Australian National University, Canberra ACT 2601, AustraliaSearch for more papers by this author, Buddini Karawdeniya https://orcid.org/0000-0002-2733-5973Department of Electronic Materials Engineering, Research School of Physics, Australian National University, Canberra ACT 2601, AustraliaSearch for more papers by this author, Yapa MNDY Bandara https://orcid.org/0000-0003-1921-8467Research School of Chemistry, Australian National University, Canberra ACT 2601, AustraliaSearch for more papers by this author & Patrick Kluth https://orcid.org/0000-0002-1806-2432Department of Materials Physics, Research School of Physics, Australian National University, Canberra ACT 2601, AustraliaSearch for more papers by this authorPublished Online:5 Jan 2024https://doi.org/10.2144/fsoa-2023-0226AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail Keywords: bioanalysisbiomarkersbiotechnologydiagnosticsnanotechnologyThe concurrent evolution of nanotechnology and artificial intelligence offers exciting opportunities to develop new revolutionary technologies. One of the most promising advances resulting from the fusion of solid-state nanopores with artificial intelligence is label free biomarker quantification and analysis and disease detection which could have a transformative impact on the healthcare sector.Solid state nanopores are nanoscale channels in a thin membrane material. These nanopores can be fabricated using different methods including but not limited to controlled breakdown, focused electron/ion-beam milling, track etch technology etc. Upon the application of a voltage across this membrane, when submerged in an electrolyte solution (usually a buffered salt solution such as LiCl, NaCl or KCl), ionic current flows through the nanopore. As biomolecules like DNA, RNA or proteins pass through the nanopore, they perturb this ionic flow, creating analyte-specific disruptions or signatures. While solid state nanopore sensing has been used for a variety of biomolecular analyses, one of the primary challenges has been in discerning between biomolecules of comparable sizes. Different proteins with similar size, for instance, could produce similar disruption patterns, making identification tricky. However, the signatures and the patterns which are generated by the translocation of different biomolecules can be analysed through artificial intelligence algorithms to determine the identity, size, shape and even conformation of the traversing molecules. Additionally, artificial intelligence algorithms can process vast amounts of data, find patterns and improve their accuracy over time without explicit programming.In recent years, researchers have demonstrated a growing interest in utilizing solid-state and biological nanopore sensing along with artificial intelligence to detect viruses [1–3], DNA and RNA modifications [4] and sequences [5], and most recently proteins [6] in a label-free fashion. We have previously demonstrated how nanopore sensing combined with artificial intelligence can be utilized to identify similar-sized proteins without labelling – indistinguishable through pulse width and height centric analysis [6]. This highlights the significant potential of artificial intelligence for recognizing different biomolecules, offering almost real-time results for determining biomarker concentrations. The ability to ‘identify’ biomolecules from complex solutions offers ground-breaking potential for medical diagnostics. Once fully developed, the workflow for quantification of biomarkers using solid-state nanopore sensing assisted by artificial intelligence is rather simple. Bodily fluids such as blood, urine, cerebrospinal fluid (CSF) can be prefiltered and subsequently measured using a portable nanopore reader employing a target specific sized nanopore for the biomarker of interest. Different features of the signals will then be automatically analysed by artificial intelligence algorithms which provide information about biomarker identity and concentration.Along with the evolution of artificial intelligence and nanopore sensing, a surge in research discovering new disease-specific biomarkers is emerging. Often a disease leads to systemic changes at the molecular and cellular level in patients. Depending on the pathological mechanisms contributing to diseases [7], e.g., inflammation, metabolic dysfunction, infection, ischemia, neuro/non-neuro degeneration, oxidative stress, apoptosis dysregulation etc., there can be an increase or decrease in one or multiple biomolecule levels in bodily fluids. For personalised measurement of these biomarker levels, new low-cost techniques that can be rolled out to point of care settings are highly desirable. Nanopore-based biomarker sensors could serve this purpose and pave the way for personalized healthcare. The ability of artificial intelligence algorithms to ‘learn’ from existing data allows it to differentiate between a myriad of molecular signatures, thus enhancing diagnostic accuracy. The inherent advantage of artificial intelligence is its iterative nature. As more data flows in, algorithms evolve, becoming more adept at making predictions and identifying anomalies. This continuous refinement ensures that diagnostic tools remain at the forefront of accuracy and reliability. The combination of solid-state nanopores and artificial intelligence promises to redefine how we detect, understand and subsequently treat diseases. Let's delve deeper into the various facets of this potential revolution.Diseases like Alzheimer's, Multiple Sclerosis (MS) and Amyotrophic lateral sclerosis (ALS) are often detected only when they have reached advanced stages. Detecting protein aggregates or specific genetic markers using nanopore technology could signal the onset of these diseases much earlier, significantly increasing the ability for successful medical intervention. Artificial intelligence can help in correlating these biomarkers with disease progression. For example, ALS is a progressively fatal condition, usually resulting in a lifespan of 3–5 years after diagnosis. Early symptoms often overlap with other neurological disorders, starting with weakness in limbs, muscle twitching, or challenges in speech and breathing. Currently, there are no conclusive tests to diagnose ALS. Typically, it takes about 12 months from the first appearance of symptoms to diagnosis, relying heavily on excluding other potential causes. Distinguishing ALS from other neuromuscular diseases with similar symptoms can be challenging for clinicians. Several biomarkers that correspond to ALS also correspond to other diseases. For example, increased neurofilament light chain (NfL) levels could indicate Alzheimer's, Multiple Sclerosis and ALS. Solid state nanopore sensing coupled with artificial intelligence could provide a means for distinguishing between these diseases, as the algorithms can be used to detect multiple biomarkers at once, hence assisting clinicians toward faster diagnosis. An earlier diagnosis for such neurodegenerative diseases could mean quicker access to emerging treatments or importantly, a quicker exclusion of ALS as the cause of the symptoms.Another use of artificial intelligence and nanopores is in the detection of viruses. The importance of rapid, low-cost, and regular screening of pathogens was essential during the COVID-19 pandemic. During such virus-related outbreaks, time is of the essence. Traditional culture methods can sometimes take days to identify pathogens. Nanopore devices, on the other hand, can detect pathogens in almost real-time at low concentrations. artificial intelligence can then classify these pathogens, guiding treatment and/or quarantine decisions. For example, M. Taniguchi and colleagues [1] have used solid state nanopores and artificial intelligence to detect SARS-CoV-2 from saliva.Nanopore technology with the aid of artificial intelligence can also provide personalised healthcare. Nanopore sensing can profile lipids in blood, providing detailed analyses. Artificial intelligence can then correlate these profiles with cardiovascular risks, offering insights into preventive measures. For many diseases (cancer being one of the prominent ones), there's always a looming risk of recurrence post-treatment. Continuous monitoring using nanopore technology could detect molecular markers indicative of a relapse, ensuring immediate intervention. The future might even see wearable devices equipped with nanopore sensors continuously monitoring our health. artificial intelligence algorithms can provide real-time feedback, predicting potential health issues before they become severe. By combining data from nanopore devices with other health measures (like ECG, blood pressure), artificial intelligence could offer a holistic view of an individual's health, advancing preventive and personalized medicine. With the potential miniaturization and cost-reduction of nanopore devices, rural clinics and resource-limited regions could gain easier access to state-of-the-art diagnostic tools, bridging the gap in healthcare inequalities.However, there are still many hurdles to overcome. In terms of membrane materials, there is a drive to invent nanopores in novel materials as well as nanopores of different shape for high signal to noise ratio, enhanced sensitivity and selective and/or specific detection of different biomolecules [8]. In general, the raw data from nanopore experiments is often replete with noise. Whereas high bandwidth measurements are important to get more information from the biomolecule translocation, such measurements often have more noise and result in large amounts of data (up to 150 MB/s) making it computationally expensive to analyse [6,9]. Advanced artificial intelligence algorithms are crucial to discern genuine molecular signals from noise as well as fast enough to analyse such a large amount of data. Further research is needed to refine these algorithms and ensure high-fidelity readings. Beyond the technological challenges, there's a broader need for the establishment of standard protocols, calibration methodologies and practices. Consistency in applying and integrating the technology into existing diagnostic systems is essential to ensure its widespread and effective use. Additionally, as with all emerging technologies, the regulatory landscape needs to be navigated, and concerns related to data privacy and security must be addressed. Together with advances in AI, the continuing improvement of nanopore experimental design, protocols and measurement hardware will still play a significant role in the outcome of this merger.In conclusion, the convergence of nanopore sensing and artificial intelligence shows exciting future potential to redefine disease detection and biomarker analysis. While challenges remain, the future looks promising. However, collaborations, investments in research, and ethical considerations, are crucial for its success. Like all nascent technologies, it is difficult to predict how far and wide the use will spread but the rapid advancements in artificial intelligence and nanopore research suggest a trajectory of significant and far-reaching impact.Financial disclosureS Dutt was supported by an AINSE Ltd. Postgraduate Research Award (PGRA) and the Australian Government Research Training Program (RTP) Scholarship. P Kluth acknowledges financial support from the Australian Research Council (ARC) under the ARC Discovery Project Scheme (DP180100068). This research was funded in part by and has been delivered in partnership with Our Health in Our Hands (OHIOH) – a strategic initiative of the Australian National University (ANU) – which aims to transform health care by developing new personalized health technologies and solutions in collaboration with patients, clinicians and healthcare providers. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Competing interests disclosureThe authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.Writing disclosureNo writing assistance was utilized in the production of this manuscript.No writing assistance was utilized in the production of this manuscript.References1. Taniguchi M, Minami S, Ono C et al. Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection. Nat Commun. 12(1), 3726 (2021).Crossref, CAS, Google Scholar2. Arima A, Tsutsui M, Washio T, Baba Y, Kawai T. Solid-State Nanopore Platform Integrated with Machine Learning for Digital Diagnosis of Virus Infection. Anal. Chem. 93(1), 215–227 (2021).Crossref, CAS, Google Scholar3. Arima A, Tsutsui M, Harlisa IH et al. Selective detections of single-viruses using solid-state nanopores. Sci Rep. 8(1), 16305 (2018).Crossref, Google Scholar4. Wan YK, Hendra C, Pratanwanich PN, Göke J. Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data. Trends Genet. S0168952521002572 (2021). 10.1016/j.tig.2021.09.001Google Scholar5. Jena MK, Pathak B. Development of an Artificially Intelligent Nanopore for High-Throughput DNA Sequencing with a Machine-Learning-Aided Quantum-Tunneling Approach. Nano Lett. 23(7), 2511–2521 (2023).Crossref, CAS, Google Scholar6. Dutt S, Shao H, Karawdeniya B et al. High Accuracy Protein Identification: Fusion od Solid-State Nanopore Sensing and Machine Learning. Small Methods. 2300676 (2023).Crossref, Google Scholar7. Hammer GD, McPhee SJ. Pathophysiology of Disease: An Introduction to Clinical Medicine. 8th ed. McGraw-Hill Education, NY, USA (2018).Google Scholar8. Dutt S, Karawdeniya BI, Bandara YMNDY, Afrin N, Kluth P. Ultrathin, High-Lifetime Silicon Nitride Membranes for Nanopore Sensing. Anal. Chem. 95(13), 5754–5763 (2023).Crossref, CAS, Google Scholar9. Lin C-Y, Fotis R, Xia Z et al. Ultrafast Polymer Dynamics through a Nanopore. Nano Lett. 22(21), 8719–8727 (2022).Crossref, CAS, Google ScholarFiguresReferencesRelatedDetails Ahead of Print STAY CONNECTED Metrics History Received 5 October 2023 Accepted 17 October 2023 Published online 5 January 2024 Information© 2024 Future Science LtdKeywordsbioanalysisbiomarkersbiotechnologydiagnosticsnanotechnologyFinancial disclosureS Dutt was supported by an AINSE Ltd. Postgraduate Research Award (PGRA) and the Australian Government Research Training Program (RTP) Scholarship. P Kluth acknowledges financial support from the Australian Research Council (ARC) under the ARC Discovery Project Scheme (DP180100068). This research was funded in part by and has been delivered in partnership with Our Health in Our Hands (OHIOH) – a strategic initiative of the Australian National University (ANU) – which aims to transform health care by developing new personalized health technologies and solutions in collaboration with patients, clinicians and healthcare providers. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.Competing interests disclosureThe authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.Writing disclosureNo writing assistance was utilized in the production of this manuscript.No writing assistance was utilized in the production of this manuscript.PDF download

Nanopore membranes enable versatile technologies that are employed in many different applications, ranging from clean energy generation to filtration and sensing. Improving the performance can be achieved by conducting numerical simulations of the system, for example, by studying how the nanopore geometry or surface properties change the ionic transport behavior or fluid dynamics of the system. A widely employed tool for numerical simulations is finite element analysis (FEA) using software, such as COMSOL Multiphysics. We found that the prevalent method of implementing the electrolyte in the FEA can diverge significantly from physically accurate values. It is often assumed that salt molecules fully dissociate, and the effect of the temperature is neglected. Furthermore, values for the diffusion coefficients of the ions, as well as permittivity, density, and viscosity of the fluid, are assumed to be their bulk values at infinite dilution. By performing conductometry experiments with an amorphous SiO2 nanopore membrane with conical pores and simulating the pore system with FEA, it is shown that the common assumptions do not hold for different mono- and divalent chlorides (LiCl, NaCl, KCl, MgCl2, and CaCl2) at concentrations above 100 mM. Instead, a procedure is presented where all parameters are implemented based on the type of salt and concentration. This modification to the common approach improves the accuracy of the numerical simulations and thus provides a more comprehensive insight into ion transport in nanopores that is otherwise lacking.

Graphene enhanced thermoplastic composites offer the possibility of conductive aerospace structures suitable for applications from electrostatic dissipation, to lightning strike protection and heat dissipation. Spray deposition of liquid phase exfoliated (LPE) aqueous graphene suspensions are highly scalable rapid manufacturing methods suitable to automated manufacturing processes. The effects of residual surfactant and water from LPE on thin films for interlaminar prepreg composite enhancement remain unknown. This work investigates the effect of heat treatment on graphene thin films spray deposited onto carbon fibre/polyether ether ketone (CF/PEEK) composites for reduced void content. Graphene thin films deposited onto CF/PEEK prepreg tapes had an RMS roughness of 1.99 μm and an average contact angle of 11°. After heat treatment the roughness increased to 2.52 μm with an average contact angle of 82°. The SEM images, contact angle, and surface roughness measurements correlated suggesting successful removal of excess surfactant and moisture with heat treatment. Raman spectroscopy was used to characterise the chemical quality of the consolidated graphene interlayer. Spectral data concluded the graphene was 3–4 layered with predominantly edge defects suggesting high quality graphene suitable for electrical enhancement. Conductive-AFM measurements observed an increase in conductive network density in the interlaminar region after the removal of surfactant from the thin film. Heat treatment of the Control sample successfully reduced void content from 4.2 vol% to 0.4 vol%, resulting in a 149% increase in compressive shear strength. Comparatively, heat treatment of graphene enhanced samples (~ 1 wt%) reduced void content from 5.1 vol% to 2.8 vol%. Although a 25% reduction in shear strength was measured, the improved electrical conductivity of the interlaminar region extends the potential applications of fibre reinforced thermoplastic composites. The heat treatment process proves effective in reducing surfactant and thus void content while improving electrical conductivity of the interlayer in a scalable manner. Further investigations into graphene loading effects on conductive enhancement, and void formation is needed.

Proteins are arguably one of the most important class of biomarkers for health diagnostic purposes. Label‐free solid‐state nanopore sensing is a versatile technique for sensing and analyzing biomolecules such as proteins at single‐molecule level. While molecular‐level information on size, shape, and charge of proteins can be assessed by nanopores, the identification of proteins with comparable sizes remains a challenge. Here, solid‐state nanopore sensing is combined with machine learning to address this challenge. The translocations of four similarly sized proteins is assessed using amplifiers with bandwidths (BWs) of 100 kHz and 10 MHz, the highest bandwidth reported for protein sensing, using nanopores fabricated in <10 nm thick silicon nitride membranes. F‐values of up to 65.9% and 83.2% (without clustering of the protein signals) are achieved with 100 kHz and 10 MHz BW measurements, respectively, for identification of the four proteins. The accuracy of protein identification is further enhanced by classifying the signals into different clusters based on signal attributes, with F‐value and specificity of up to 88.7% and 96.4%, respectively, for combinations of four proteins. The combined use of high bandwidth instruments, advanced clustering and machine learning methods allows label‐free identification of proteins with high accuracy.

Particle irradiation using light ions and heavy ions is found to be an effective method to introduce flux-pinning centers into REBCO films and coated conductors. The degree of enhanced critical current at various conditions depends upon the size, morphology, and orientation of ion tracks. Proton irradiation to the optimised fluence results in greater isotropic enhancement at lower temperatures, the enhancement decreases as temperature increases. Silver ion irradiation on the other hand gives a greater enhancement at higher temperature but limited to particular angular ranges. We compare the results of these two types of irradiation and then produce a mixed pinning landscape with a combination of the two. We find a nearly isotropic enhancement in Ic at lower temperatures and an enhancement about the c -axis direction, similar but broader than silver irradiation alone, at higher temperatures.

Rapid and selective identification of biomarkers is a coveted need recapitulated by the ongoing pandemic that pierced the very fabric of human health and wellbeing. Moreover, there is a constant interest and demand for portable technologies that can meet the above-mentioned. Nanopores—a nanoscale aperture through an otherwise impermeable membrane—have demonstrated tremendous potential to this extent, which is further facilitated by their ability to detect and characterize a wide range of bio/synthetic molecules and particles (e.g., DNA, proteins, viruses). Fundamentally, they register analyte-specific information through resistive (or conductive) pulses as an analyte transverses the pore in response to an applied electric field. However, the presence of solid-state nanopores (SSNs) in the commercial space is still meager. Features such as stability, lifetime, resilience against clogging, pore noise, and signal magnitude are key for the progression of SSNs beyond a laboratory setup. The membrane and solution chemistry during fabrication was found to play an integral role where pores remained open for hours surpassing most recorded statistics concerning the five before-mentioned features. A wide range of solution chemistries (and breakdown conditions) was investigated to understand the chemistry of fabrication and its impact on the pore performance to push the existing boundaries of the technology. Afterward, to uncover finer molecular-scale details, especially those concerning smaller and fast-moving proteins, higher bandwidths (i.e., 10 MHz) were used. This enables the detection of events that last a few hundred nanoseconds which were previously impossible due to electronic limitations. With such advancements, the enormity of available data would enable the seamless coupling of machine learning for the identification of targets in complex mixtures such as serum. Thus, such developments would enable SSNs to perform well under real-world conditions and complex samples.

Nano-porosity in amorphous Ge formed by swift heavy ion irradiation displays a peculiar self-organisation process. Initially almost randomly distributed pores grow with increasing irradiation fluence and segregate in layers orientated parallel to the sample surface. This self-organisation mechanism depends on the ion energy, thickness of the amorphous Ge layer and the angle of ion incidence and shows a characteristic length depending on ion energy and irradiation angle. Molecular dynamics simulations of individual ion tracks show that voids form due to the transition from the low-density amorphous to the high-density liquid phase, which also gives rise to a flow directed away from large pores and surfaces. The flow results in a characteristic distance from surfaces and larger pores, below which new voids do not form, and supports the formation of voids at the amorphous/ crystalline interface. Simulations also demonstrate that, while direct impacts can reposition small voids, partial or nearby impacts promote their growth at the same location. These processes are plausible drivers for the self -organization.

Using synchrotron-based small angle X-ray scattering, ion tracks created in polypropylene foils with different antioxidant contents were investigated. Tracks were created by irradiation with 197Au, 209Bi, and 132Xe ions of energies 2.2 GeV, 710 MeV, and 160 MeV, respectively. The influence of antioxidant concentration in the polymer foils and aging of the samples on the structure of the ion tracks was explored. Polypropylene foils with high antioxidant content show a cylindrical track structure with a highly damaged core with significant mass loss and a gradual transition to the undamaged material. The size of the ion track can directly be correlated to the energy loss. On the other hand, ion tracks in low antioxidant content polypropylene foils exposed to Au/Bi ions reveal a cylindrical core shell structure with an over-dense shell area and a core region that is less dense than the pristine polymer. Oxygen uptake in the foils by the free radicals produced in the shell during the ion irradiation process was attributed to this structure due to prolonged exposure to ambient atmosphere. An overall mass increase was observed for these samples, consistent with the SAXS measurements and additional oxidation in a damaged halo produced by tracks.

Nanopore membranes are a versatile platform for a wide range of applications ranging from medical sensing to filtration and clean energy generation. To attain high-flux rectifying ionic flow, it is required to produce short channels exhibiting asymmetric surface charge distributions. This work reports on a system of track etched conical nanopores in amorphous SiO$_2$ membranes, fabricated using the scalable track etch technique. Pores are fabricated by irradiation of 1 $\mu$m thick SiO$_2$ windows with 2.2 GeV $^{197}$Au ions and subsequent chemical etching. Structural characterisation is performed using atomic force microscopy (AFM), scanning electron microscopy (SEM), small angle X-ray scattering (SAXS), ellipsometry, and surface profiling. Conductometric characterisation of the pore surface is performed using a membrane containing 16 pores, including an in-depth analysis of ionic transport characteristics. The pores have a tip radius of (5.7 $\pm$ 0.1) nm, a half-cone angle of (12.6 $\pm$ 0.1)$^{\circ}$, and a length of (710 $\pm$ 5) nm. The $pK_a$, $pK_b$, and $pI$ are determined to 7.6 $\pm$ 0.1, 1.5 $\pm$ 0.2, and 4.5 $\pm$ 0.1, respectively, enabling the fine-tuning of the surface charge density between +100 and -300 mC $m^{-2}$ and allowing to achieve an ionic current rectification ratio of up to 10. This highly versatile technology addresses challenges that contemporary nanopore systems face, and offers a platform to improve the performance of existing applications.