New Atomic-Scale Method Promises Longer-Lasting Batteries

Chemists at the University of East London have unveiled a measurement technique that could accelerate the search for better battery materials, potentially reshaping how researchers across the energy field identify the chemical building blocks of next-generation storage devices. The method, developed with collaborators and published in the Journal of the American Chemical Society, centres on a family of chemical species that most people never think about but that quietly determine how well batteries work: anions, the negatively charged particles that shuttle their way through electrolytes and cling to electrode surfaces in nearly every electrochemical system in commercial use today.

The core insight of the new study is deceptively simple. Different anions interact with surrounding materials in markedly different ways, and one of the most important determinants of that behaviour is how tightly each atom within the anion holds on to its electrons. An anion that readily shares electron density with a neighbouring electrode material will behave very differently in a battery than one that clings stubbornly to its charge. Until now, quantifying this so-called electron-donating tendency for a wide range of anions has required laborious, expensive laboratory experiments, one chemical at a time, which has slowed the pace of materials discovery considerably.

The University of East London team, working alongside their co-authors, attacked the problem with a two-pronged strategy combining experiment and computation. On the experimental side, they turned to X-ray photoelectron spectroscopy, or XPS, a technique that works by firing X-rays at a material and carefully measuring the energies of the electrons that are knocked loose in the process. Because each element and each chemical bonding environment produces a characteristic signature, XPS allows researchers to identify precisely which atoms are present in a sample and, crucially, how those atoms are bonded to their neighbours. By examining the photoelectron spectra of anions interacting with battery-relevant surfaces, the team could read off directly how strongly specific atoms within each anion were gripping their electrons.

That measurement alone would have been a useful incremental advance, but the researchers went a step further. They then built computer models capable of recreating those spectroscopic measurements virtually, calibrating the simulations against the experimental XPS data until the two agreed. Once the computational framework was validated, it could be used to predict the electron-donating behaviour of anions that had never been measured in the laboratory, effectively generating a fast, inexpensive screening tool. In practical terms, this means chemists could evaluate thousands of candidate anions on a computer and shortlist only the most promising ones for physical synthesis and testing, dramatically reducing the time and cost that typically separate a good idea from a working material.

The team describes the resulting quantities as element-specific donor numbers for anions, a metric that captures, atom by atom, the willingness of a negatively charged species to share electron density with its environment. Donor numbers have a long history in chemistry as a way of ranking how strongly solvents and other species bind to central atoms, but extending the concept to individual atoms within anions, and grounding it in hard spectroscopic data rather than indirect inference, is what distinguishes the new work. The approach gives researchers a common, quantitative language for comparing anions that previously had to be assessed through scattered, experiment-specific observations.

The immediate application is battery chemistry, where the choice of anion in an electrolyte influences everything from ionic conductivity to the stability of the electrode-electrolyte interface and, ultimately, how many charge and discharge cycles a cell can survive before degrading. Fluorinated anions, for example, are prized in lithium-ion and next-generation lithium metal batteries precisely because of the way their electron-withdrawing behaviour shapes protective interphases on electrode surfaces. With a validated predictive model in hand, researchers designing such electrolytes can now reason about candidate anions in terms of measured and modelled electron-donation properties rather than relying on trial-and-error, focusing their efforts on the materials that show the most promise.

Dr Richard Matthews, Senior Lecturer of Physical and Computational Chemistry at the University of East London and co-author of the study, emphasised the wider significance of the advance. We now have a much clearer picture of how anions interact with other materials, and that opens up some exciting possibilities, he said. By being able to predict these interactions, we can focus our efforts on the materials that show the most promise for better batteries and beyond. His framing captures the essential economic argument behind the work: in a field where every new candidate material must traditionally pass through synthesis, characterisation and testing, any method that filters out dead ends on a computer can save months of effort and substantial research funding.

The implications, however, stretch well beyond the electrochemistry laboratory. The researchers note that the measurement-and-modelling workflow could eventually be scaled up into a comprehensive database spanning many different elements and bonding environments. Such a dataset would be a valuable raw material for machine learning and artificial intelligence systems, which thrive on large volumes of high-quality, consistently defined measurements. Trainable models fed with element-specific donor numbers could surface unexpected patterns in chemical behaviour, propose novel anion structures for synthesis, and generally compress the discovery cycle for a wide range of chemical processes, not only those relevant to energy storage but also catalysis, corrosion science and medicinal chemistry, where anion interactions play equally decisive roles.

The study also illustrates a broader trend in modern materials research, in which the boundary between measurement and prediction is becoming increasingly porous. Spectroscopy provides the ground truth that anchors computational models to reality, while the models extend the reach of a limited number of experiments to a vast space of unmeasured chemistry. This virtuous loop, in which each new experimental data point improves the accuracy of predictions across an entire chemical family, is quickly becoming the standard playbook for labs hoping to compete in the race toward improved batteries, including the solid-state systems and sodium-based chemistries that many companies and national programmes are pursuing as alternatives to today’s lithium-ion technology.

For now, the University of East London team and their collaborators have laid the foundation, publishing a peer-reviewed method that others can adopt, replicate and extend. The paper, titled Element-Specific Donor Numbers for Anions, appeared in the Journal of the American Chemical Society under the DOI 10.1021/jacs.6c06567. If the framework spreads as widely as its authors hope, the humble anion, long treated as an afterthought in popular discussions of battery technology, may finally receive the systematic, quantitative attention it deserves, and consumers could one day reap the benefits in devices that hold their charge longer, degrade more slowly and cost less to develop.

Subject of Research: Element-specific donor numbers for anions as a predictive tool for battery material discovery

Article Title: Scientists discover method that could lead to longer-lasting batteries

Article References: Scientists discover method that could lead to longer-lasting batteries. (n.d.). Original publication

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Keywords: batteries, anions, X-ray photoelectron spectroscopy, University of East London, electrolytes, computational chemistry, machine learning, energy storage, materials science, donor numbers, chemical discovery, electrochemistry