The ever-growing need for radiopharmaceuticals, i.e., compounds containing pharmaceutical drugs and radioisotopes used for medical diagnostic imaging (SPECT/PET scan) and treating neoplasms, is significantly leading to an increased demand for such substances in hospitals and clinics worldwide. Currently, most large-scale productions of radioisotopes required for radiopharmaceuticals are carried out in research reactors, via the fission of highly enriched uranium. However, because large amounts of radioactive waste are produced as byproducts in this process, new greener methods are needed for radioisotope production. This work presents an integrative literature review and summarizes enriched uranium-free methods for radioisotope production, accomplished through the adoption of new reaction routes, distinct acceleration technologies, or by using other physical processes. This review considered forty-eight studies published from 2010 to 2021 on three established virtual databases. Among these selected works, a cyclotron is the most adopted HEU-free method for radioisotope production, and ${}^{44}$Sc, ${}^{68}$Ga, and ${}^{99\mathrm{m}}$Tc are the medical radioisotopes most often reported as produced by using the investigated HEU-free production methods.

While laser wakefield acceleration (LWFA) in the bubble regime demands ultra-short, high-peak-power laser pulses, operation in the self-modulated regime (SM-LWFA) works with more relaxed pulse conditions, albeit at the cost of lower beam quality. Modern laser systems can deliver few-terawatt pulses with tens of femtoseconds at kilohertz repetition rates. These systems are well-suited for developing SM-LWFA applications where high average energy and charge are prioritized over beam quality. Such beams could be used to generate high-energy bremsstrahlung photons, capable of triggering photonuclear reactions to produce radioisotopes like molybdenum-99. This isotope decays into technetium-99m, the most widely used medical radioisotope, with over 30 million applications worldwide per year. This work explores the use of Bayesian optimization to maximize the energy and charge of electron beams accelerated via SM-LWFA. Particle-in-cell (PIC) simulations model a 5 TW, ∼60 fs-long (FWHM) Gaussian laser pulse, propagating through tailored hydrogen gas-density profiles. In these simulations, over multiple iterations, the algorithm optimizes a set of input parameters characterizing the gas-density profile and the laser focal position. Three distinct profiles, with lengths ranging from 200 to 400 μm and combining ramps and plateaus, were investigated. Optimal configurations produced electron beams with median energies ranging from 14 to 17 MeV and charges of 600 to 1300 pC, considering electrons with energies above 8 MeV. Preliminary estimates of molybdenum-99 yields for the optimal beams were obtained using their phase space data from PIC simulations as radiation sources in Monte Carlo simulations irradiating a tantalum-molybdenum target.

Plasma wakefields driven by high power lasers or relativistic particle beams can be orders of magnitude larger than the fields produced in conventional accelerating structures. Since the plasma wakefield is composed not only of accelerating but also of decelerating phases, this paper proposes to utilize the strong decelerating field induced by a laser pulse in the plasma to absorb the beam energy, in a scheme known as the active plasma beam dump. The design of this active plasma beam dump has considered the beam output by the EuPRAXIA facility. Analytical estimates were obtained, and compared with particle-in-cell simulations. The obtained results indicate that this active plasma beam dump can contribute for more compact, safer, and greener accelerators in the near future.

Solid-state plasma wakefield acceleration has recently attracted attention as a novel method for achieving unprecedented ultrahigh acceleration gradients on the order of 1 TeV/m or beyond. In this context, recent advancements in nanofabrication techniques have opened up the possibility of creating structured plasmas with tailored properties. For instance, the utilization of carbon nanotube (CNT) bundles holds great potential for generating stable plasmas with electron densities reaching as high as $$10^{22}$$ 10 22 $$\hbox {cm}^{-3}$$ cm - 3 , i.e., orders of magnitude higher than conventional gaseous plasmas. As part of a new collaborative effort called NanoAc, we have conducted particle-in-cell (PIC) simulations to investigate laser wakefield acceleration in nanostructured solid-state plasmas based on CNT arrays. Our results confirm the attainment of wakefields at the TV/m scale. Additionally, we observed self-injection, sub-femtosecond bunch formation, and electron acceleration in micrometer-scale targets, yielding kinetic energies on the order of a few tens of MeV. These findings open up promising possibilities to design novel ultracompact accelerators and radiation sources. In this paper, we report recent simulation results from the NanoAc collaboration and describe ongoing efforts toward future experimental tests at available laser facilities.

Monte Carlo simulations are widely used to estimate radioisotope production through nuclear reactions. However, achieving high accuracy typically requires simulating more than 10⁸ particles, which is computationally demanding, particularly when many simulations are required, such as in optimization loops. In a previous study, we demonstrated that molybdenum-99 yields from bremsstrahlung-driven photonuclear reactions induced by laser accelerated electron beams can be estimated up to four orders of magnitude faster by employing a multigroup energy representation, in which a limited set of Monte Carlo precomputed group yields is used to reconstruct the total yield for an arbitrary source spectra. The present work expands this approach by showing that the multigroup representation also provides a deeper understanding of the relative contribution of different electron energies to the final isotope yield. Applying the method to 640 different spectra revealed how the choice of group structure—such as the number of groups and their energy widths—affects both accuracy and efficiency. The results show that the proposed approach reproduces full Monte Carlo yield estimates with median errors below 5%, reaching values as low as 0.3%.