A inovação melhora muito a resolução ultrarrápida alcana§a¡vel com lasers de elanãtrons livres de raios-X
Um dispositivo XFEL éuma combinação poderosa de acelerador departículas e tecnologia de laser que produz pulsos de raios X extremamente brilhantes e ultracurtos para pesquisas cientaficas.
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)
Representação artastica da medição XFEL com gás neon. O atraso inerente entre a emissão de fotoelanãtrons e elanãtrons Auger leva a uma elipse característica nos dados analisados. Em princapio, a posição de pontos de dados individuais ao redor da elipse pode ser lida como os ponteiros de um rela³gio para revelar o tempo preciso dos processos de decaimento. Crédito: Daniel Haynes e Ja¶rg Harms / Instituto Max Planck para a Estrutura e Dina¢mica da Matanãria.
Uma grande equipe internacional de cientistas de várias organizações de pesquisa, incluindo o Laborata³rio Nacional de Argonne do Departamento de Energia dos EUA (DOE), desenvolveu um manãtodo que melhora drasticamente a resolução de tempo já ultrarrápida alcana§a¡vel com lasers de elanãtrons livres de raios-X (XFELs). Isso pode levar a descobertas sobre como projetar novos materiais e processos químicos mais eficientes.
Um dispositivo XFEL éuma combinação poderosa de acelerador departículas e tecnologia de laser que produz pulsos de raios X extremamente brilhantes e ultracurtos para pesquisas cientaficas. "Com essa tecnologia, os cientistas agora podem rastrear processos que ocorrem em milhões de bilionanãsimos de segundo (femtossegundos) em tamanhos menores que a escala atômica", disse Gilles Doumy, fasico da divisão de Ciências Químicas e Engenharia de Argonne. "Nosso manãtodo torna possível fazer isso por tempos ainda mais rápidos."
Uma das aplicações mais promissoras dos XFELs tem sido nas ciências biológicas. Nessa pesquisa, os cientistas podem capturar como os processos biola³gicos fundamentais para a vida mudam ao longo do tempo, mesmo antes que a radiação dos raios X do laser destrua as amostras. Na física e na química, esses raios-X também podem lana§ar luz sobre os processos mais rápidos que ocorrem na natureza, com uma velocidade de obturador que dura apenas um femtossegundo. Esses processos incluem a formação e a quebra de ligações químicas e as vibrações de a¡tomos emsuperfÍcies de filme fino.
Por mais de uma década, os XFELs forneceram pulsos de raios-X intensos de femtossegundo, com incursaµes recentes no regime de subfemtossegundo (attossegundo). No entanto, nessas escalas de tempo minaºsculas, édifacil sincronizar o pulso de raios-X que desencadeia uma reação na amostra e o pulso de laser que a "observa". Esse problema échamado de instabilidade de tempo.
O autor principal Dan Haynes, estudante de doutorado no Instituto Max Planck para a Estrutura e Dina¢mica da Matanãria, disse: "a‰ como tentar fotografar o final de uma corrida quando o obturador da ca¢mera pode ser ativado a qualquer momento nos dez segundos finais."
Para contornar o problema de jitter, a equipe de pesquisa criou uma abordagem pioneira e altamente precisa chamada de "listras de attosegundo auto-referenciadas". A equipe demonstrou seu manãtodo medindo um processo de decaimento fundamental no gás neon no Linac Coherent Light Source, um DOE Office of Science User Facility no SLAC National Accelerator Laboratory.
Doumy e seu orientador na anãpoca, o professor Louis DiMauro da Ohio State University, propuseram a medição pela primeira vez em 2012.
No processo de decaimento, chamado decaimento Auger, um pulso de raios-X catapulta os elanãtrons do núcleo ata´mico da amostra para fora de seu lugar. Isso leva a sua substituição por elanãtrons nas camadas atômicas externas. Amedida que esses elanãtrons externos relaxam, eles liberam energia. Esse processo pode induzir a emissão de outro elanãtron, conhecido como elanãtron Auger. Danos por radiação ocorrem devido aos intensos raios-X e a emissão contanua de elanãtrons Auger, que podem degradar rapidamente a amostra. Apa³s a exposição aos raios-X, os a¡tomos de nanãon também emitem elanãtrons, chamados fotoelanãtrons.
Depois de expor os dois tipos de elanãtrons a um pulso de laser externo "estriado", os pesquisadores determinaram sua energia final em cada uma das dezenas de milhares de medições individuais.
"A partir dessas medições, podemos acompanhar a decadaªncia do Auger no tempo com precisão subfemtossegundo, mesmo que o jitter de tempo seja cem vezes maior", disse Doumy. "A técnica se baseia no fato de que os elanãtrons Auger são emitidos um pouco mais tarde do que os fotoelanãtrons e, portanto, interagem com uma parte diferente do pulso de laser."
Esse fator constitui a base da técnica. Ao combinar tantas observações individuais, a equipe foi capaz de construir um mapa detalhado do processo de decadaªncia física. A partir dessa informação, eles puderam determinar o atraso de tempo caracterastico entre o fotoelanãtron e a emissão do elanãtron Auger.
Os pesquisadores tem esperana§a de que as estrias auto-referenciadas tera£o um amplo impacto no campo da ciência ultrarrápida. Essencialmente, a técnica permite que a espectroscopia de streaking de attossegundo tradicional seja estendida a XFELs em todo o mundo conforme eles se aproximam da fronteira de attossegundo. Desta forma, streaking auto-referenciado pode facilitar uma nova classe de experimentos, beneficiando-se da flexibilidade e extrema intensidade dos XFELs sem comprometer a resolução do tempo.